*Article* **Biomimetic Tactile Sensors with Bilayer Fingerprint Ridges Demonstrating Texture Recognition**

#### **Eunsuk Choi 1, Onejae Sul 2, Jusin Lee 1, Hojun Seo 1, Sunjin Kim 1, Seongoh Yeom 1, Gunwoo Ryu 1, Heewon Yang 1, Yoonsoo Shin 1 and Seung-Beck Lee 1,2,\***

Received: 2 September 2019; Accepted: 23 September 2019; Published: 25 September 2019

**Abstract:** In this article, we report on a biomimetic tactile sensor that has a surface kinetic interface (SKIN) that imitates human epidermal fingerprint ridges and the epidermis. The SKIN is composed of a bilayer polymer structure with different elastic moduli. We improved the tactile sensitivity of the SKIN by using a hard epidermal fingerprint ridge and a soft epidermal board. We also evaluated the effectiveness of the SKIN layer in shear transfer characteristics while varying the elasticity and geometrical factors of the epidermal fingerprint ridges and the epidermal board. The biomimetic tactile sensor with the SKIN layer showed a detection capability for surface structures under 100 μm with only 20-μm height differences. Our sensor could distinguish various textures that can be easily accessed in everyday life, demonstrating that the sensor may be used for texture recognition in future artificial and robotic fingers.

**Keywords:** biomimetic; tactile sensor; fingerprint ridge; piezoelectric sensor; texture discrimination

#### **1. Introduction**

The human ability of tactile sensing using the finger plays an essential role in object manipulation and our interaction with the external environment. When making contact with an object, we can recognize its texture using tactile senses made through mechanoreceptors distributed throughout the skin [1]. Therefore, research into tactile sensing attempts to mimic the mechanoreceptors in the human skin, their pressure sensing ability, and their distribution in the skin. Recently, many groups have reported on pressure sensors that demonstrate basic artificial tactile feeling and texture pattern recognition for prosthesis and robotics applications. They have placed pressure sensors at the digits of robotic arms to grip and manipulate objects and have arranged pressure sensors in an array form to detect the shape of an object [2–5]. Electronic skin instrumented with pressure sensors for prosthesis application has also been reported [6–8].

Many attempts at improving tactile sensing ability have focused on enhancing pressure sensing sensitivity in the belief that higher pressure sensitivity would lead to enhanced sensibility of the contacting object. To enhance sensitivity of the pressure sensors, a capacitor-type pressure sensor was demonstrated, which was able to detect only a few milligrams of tactile pressures [9–15]. However, the high pressure sensitivity was achievable only at low spatial resolution, limiting the fine-texture detecting capability. A closer inspection of the human skin showed that the sensitivity and distribution of the mechanoreceptors are not the only criteria for tactile sensing.

When a human finger slides over an object's surface, the fingerprint ridge structures interact with the object's surface structure and generate vibrational signals, which are then transmitted to the mechanoreceptors lying under the epidermis. The generated action potential pulses in the mechanoreceptors travel to the brain, where they are interpreted as shear interaction and are used for recognizing the "fine texture" of the object [16]. To detect fine surface textures, ridge structures mimicking human fingerprint ridges have been applied to tactile sensors [17–21]. The biomimetic ridge structures showed the ability to detect a few hundred micrometers of scale surface structures [18], and it was found that biomimetic ridge structures are an essential element for material and textural identification. Despite there being many studies on tactile sensors with biomimetic ridge structures, only a few have reported studies on the ridge structure itself, e.g., elasticity, dimension, for the improvement of texture recognition.

In this study, we developed a biomimetic tactile sensor with a surface kinetic interface (SKIN) that imitates two elements of the human skin: epidermal fingerprint ridges and the epidermis. Our tactile sensor is composed of a SKIN (biomimetic epidermal fingerprint ridges and supporting epidermal board) layer and a polyvinylidene difluoride (PVDF) piezoelectric polymer layer that acts as a vibration sensor, which functions similarly to a fast-adapting mechanoreceptor. We evaluated the effectiveness of the SKIN layer in transferring surface shear forces into vertical vibrations while varying the elasticity and geometrical factors of the epidermal fingerprint ridges and epidermal board. We found that the tactile sensitivity was increased when using a softer epidermal board and a harder epidermal fingerprint ridge. The biomimetic tactile sensor with SKIN showed detectability for sub-100-μm surface structures and 20-μm height differences. Our sensor produced signals that made it possible to discern different types of paper, leather, and fabrics. It was also possible to distinguish a real human finger from its replica. The developed biomimetic tactile sensor may be applied to future artificial fingers for texture detection and recognition.

#### **2. Surface Kinetic Interface (SKIN)**

An illustration of human skin on a fingertip can be seen in Figure 1a. From a tactile sensing perspective, the human skin is composed of two parts, the epidermis and the dermis. The epidermis comes in contact with the outside world and transfers kinetic interactions to the dermis. In the dermis, various mechanoreceptors convert the mechanical stimulus into action potential signals. The mechanoreceptor illustrated in Figure 1a is a Meissner's corpuscle, which generates an action potential responding to changes in the surface strain [22]. In the finger epidermis, the top layer forms epidermal fingerprint ridges (ERs) that interact directly with the contact material. Underneath it, there are layers of cells that we call the epidermal board that collectively act as a force transfer layer [23]. We designed our tactile sensor skin to functionally mimic the outer skin of the human fingertip: the SKIN layer that consists of ERs on top of a supporting epidermal board. The PVDF piezoelectric thin-film layer acts as an array of vibration sensors mimicking fast-adapting mechanoreceptors (see Figure 1b). The way shear forces are detected in our tactile sensor is quite similar to the human fingertip. When an object horizontally touches the SKIN surface, a lateral shear force is applied to the ERs. Torque is then generated at the contact point between the ERs, with the epidermal board acting as an axis of rotation. Therefore, the contact shear force is transformed into torque and then transferred as vertical pressure to the PVDF layer. The PVDF layer is a piezoelectric material that generates a voltage signal when applied pressure deforms its polarized molecular structures. The magnitude of the generated voltage signal depends on the magnitude of the lateral shear force; as higher shear force induces higher vertical pressure on the PVDF. In addition, the frequency of the voltage signal depends on the frequency of contact with the ERs. This depends on the relative speed of the scanning object and the period of surface protrusions on the object's surface that makes contact with the ERs. Therefore, the magnitude and frequency of the generated output voltage depend mostly on the surface roughness and hardness. This will be further elaborated in Section 4. Although our tactile sensor is biomimetic in the SKIN structure, there are differences from human skin in that unlike each mechanoreceptor

individually detecting the input stimulus, the PVDF layer acts as a continuous distribution of sensors, with the detected output signal being a parallel integration of all the voltage signals generated over the whole surface of the sensor at that particular time interval. In mapping the surface topography of a contacting material, this would be disadvantageous, since any information generated through the spatial distribution of contact pressure points within the sensor area would not be distinguishable. However, it will be shown that for detecting the texture of the contacting surface, the parallel integration of the voltage signals becomes advantages. This sensing mechanism is similar to vibrotactile sensation in the duplex theory of human tactile perception using fast-adapting mechanoreceptors [24].

**Figure 1.** Schematic illustration and shear detecting mechanism of (**a**) the human fingertip and (**b**) the biomimetic tactile sensor.

#### **3. Fabrication**

The Young's modulus of human fingerprint ridges is known to be about three times higher than that of the epidermal board, which is believed to increase the durability of the outer skin layer [25]. To mimic this configuration of the human epidermis, we fabricated harder ERs using SU-8 (Microchem, Westborough, MA, USA), which had a Young's modulus of ~3 GPa, and a soft epidermal board (using polydimethylsiloxane (PDMS)), which had a Young's modulus of ~750 kPa. For efficient force transfer, ERs should not absorb the contact force through elastic deformation, while the epidermal board should be flexible enough to localize the deformation and allow the ER to tilt with the applied contact shear force. To test the effectiveness of this configuration, we fabricated four types of SKINs with differing mechanical configurations: hard or soft ERs and hard or soft epidermal boards. We used SU-8 and polyethylene terephthalate (PET) as the harder material (*E* ≈ 3 GPa) and PDMS as the softer material (*E* ≈ 750 kPa) (see Figure 2a). The geometry of all SKINs was identical, with the thickness of the epidermal board at 50 μm, the height of the ER at 50 μm, and the width of the ER plateau and valley both at 100 μm, giving the fingerprint pattern a 200-μm period, which is roughly half of the period of human fingerprints (0.3–0.5 mm) [26].

Figure 2b shows the fabrication processes of the different SKINs. For SKIN #1, an epidermal board was fabricated by spin-coating 50-μm-thick SU-8 on an oxidized silicon substrate. Then the SU-8 2075 was coated again at a 50-μm thickness, and ERs were patterned by using optical lithography. Then the fabricated SKIN #1 was lifted off through HF etching of the oxide underneath. For SKIN #2, a 50-μm-thick SU-8 ER master was patterned using optical lithography. A PET film was attached to the ER master and was dipped into the PDMS. The viscosity of the PDMS was low enough to fill the vacant spaces between the SU-8 master and the PET film. After the PDMS was cured, the fabricated SKIN #2 was peeled off from the master. The PET film for SKIN #2 was etched by CF4 physical plasma to form nanobrush structures, which promoted adhesion with PDMS by increasing the contact surface area [27,28]. For SKIN #3, a 50-μm-thick SU-8 layer and 50-μm-thick PDMS were coated on the oxidized silicon substrate. This SU-8 layer was helpful for the separation of the PDMS layer from the substrate after completion of SKIN #3. Then the surface of the PDMS was treated by oxygen plasma for better adhesion with a 2-μm-thick SU-8 layer coated on PDMS. This SU-8 thin layer works as the adhesive layer between the PDMS and the following SU-8 ERs. A 50-μm-thick SU-8 ER was patterned by using optical lithography. Finally, the fabricated SKIN #3 was peeled off from the bottom SU-8 layer on the

substrate. For SKIN #4, a 50-μm-thick SU-8 ER master was patterned, and 50-μm-thick PDMS was coated on the ER master. After the PDMS was cured, SKIN #4 was peeled off.

**Figure 2.** (**a**) Four types of surface kinetic interfaces (SKINs) with differing mechanical configurations and (**b**) their fabrication processes.

Figure 3a shows an SEM image of the fabricated SKIN #3. The fabricated SKIN was attached to a 28-μm-thick PVDF sensor (DT1-028K, Measurement Specialties, Hampton, VA, USA) to complete the flexible tactile sensor device structure (Figure 3b). The PVDF sensor was 12 mm wide and 30 mm long and had a piezoelectric voltage constant (g33) of −0.33 Vm/N. Figure 4 shows the measurement set-up, which had a 2-axis (x–z) motorized stage with 2 μm resolution. The scanning speed was controlled by the motorized *x* axis, and the contact depth was controlled by a motorized *z* axis. A PET tip with 5 mm of width and 125 μm in thickness was used for applying shear force to the ER (see inset). The PET tip was scanned across the SKIN surface without any vertical pressure, and the response voltage signal of the PVDF sensor was monitored using an oscilloscope. The tactile signal was obtained by subtracting the reference noise (which was obtained when the motorized stage was scanned above a sample without touching) from the measured PVDF signal in the frequency domain [29].

**Figure 3.** Images of a fabricated biomimetic tactile sensor with SKIN: (**a**) a false-color SEM image of the fabricated SKIN #3, which was composed of (yellow) SU-8 epidermal fingerprint ridges with a 200-μm-period and a 50-μm height, and a (blue) polydimethylsiloxane (PDMS) epidermal board with a 50-μm thickness; (**b**) an optical image of the fabricated flexible biomimetic tactile sensor with SKIN.

**Figure 4.** Optical image of the measurement set-up, where the inset shows an optical image of a SKIN and a hovering polyethylene terephthalate (PET) tip.

#### **4. Tactile Sensitivity Dependent on SKIN Configuration**

The shear-induced torque applied to the ER structure acts to amplify the contact information [17,18]. To test the effect of having an ER on the sensor surface, the PET tip was scanned at 2.5 cm/s on the tactile sensor with SKIN #1 and the tactile sensor with only an SU-8 epidermal board without an ER. In the case of the tactile sensor with an ER, a significant fluctuation was observed in the sensor output signal after 0.2 s (see Figure 5a). Figure 5b shows the sensor signal between 0.3 and 0.4 s. It was observed that the main period of signal fluctuation was about 8 ms. However, in the case of the tactile sensor without ER, a periodic signal was not observed except for a 15-mV fluctuation induced by power noise (see Figure 5c). For more detailed analysis, we converted the time-dependent PVDF output signal to the frequency domain using fast Fourier transform (FFT). Figure 5d shows the denoised FFT results of Figure 5b,c. The tactile sensor without ERs showed frequency spectra in the low frequency range. This feature was induced by the stick-slip of the PET tip on the epidermal board. However, it was observed that there was a noticeable peak around 125 ± 5 Hz from the tactile sensor with an ER. Judging from the fact that *f* = *v*/*p*, where *f* is the signal frequency, *v* is the scanning speed, and *p* is the structural period, the distinct peak of about 125 Hz was induced by periodical interaction between the PET tip and the ER. This result shows that the ERs generate periodic contact information at a specific frequency. We defined this peak frequency induced by the period of ERs and the scanning speed as *f* ER.

**Figure 5.** Measurement results of tactile sensor with or without epidermal fingerprint ridges (ERs) when a PET tip was scanned at 2.5 cm/s on the tactile sensor: (**a**) the polyvinylidene difluoride (PVDF) output signal of a tactile sensor with ridge structure; (**b**) the region of 0.3–0.4 s in (a); (**c**) the PVDF output signal of a tactile sensor without ridge structure; (**d**) the fast Fourier transform (FFT) results of (b,c).

To test which SKIN layer composition had the most efficient transfer of surface interactions to the PVDF sensor, we performed the PET tip sliding measurement over the four different SKIN types and compared their FFT results. It can be seen in Figure 6 that all of the SKINs showed a peak in the FFT spectrum around *f* ER = 125 Hz, which corresponded with the 2.5 cm/s scanning speed of the PET tip. The slight variation in the peak frequency may have been due to swelling in the polymer layers that was created during the different fabrication processes, causing expansion in the epidermal boards. In comparing the magnitudes of *f* ER, we found that SKIN #3 gave the highest magnitude of −60.50 dB, followed by SKIN #1 at −64.90 dB: both had SU-8 ERs. The harder SU-8 would have less elastic deformation than PDMS would, absorbing less of the lateral shear force applied by the sliding PET tip. Hence, as the PET tip slid over the SU-8 ERs, its interaction was more pronounced than with the SKINs with PDMS ridge structures, resulting in the signal magnitude of *f* ER being 6.85 times higher for the sensor in SKIN #3 than in SKIN #2 (−68.86 dB), which both had PDMS epidermal boards. Comparing SKIN #3 and SKIN #1, the signal magnitude of *f* ER was 2.75 times lower for the sensor with SKIN #1, which had the harder SU-8 epidermal board. This is understandable if one considers that a softer epidermal board would allow the ERs to tilt more with the applied shear force and create higher local deformation, while the harder SU-8 would distribute the vertical force over a wider area with lower local deformation, which would reduce the sensor signal. Therefore, it is reasonable that SKIN #4 (with soft PDMS ERs and a hard PET epidermal board) showed the lowest magnitude of *f* ER. The configuration of the material mechanical properties of SKIN #3 best mimicked that of the human epidermis (with harder fingerprint ridges on top of a softer epidermal board), which gave it the highest amplifying ability. From these results, we found that the human epidermal structure is designed not only to increase ductility but also to efficiently transfer the input stimulus on the skin to the mechanoreceptors.

The heights of the ER structures could also affect the amount of shear force transferred to the PVDF sensor, since the generated torque would depend on the length of the displacement vector. To investigate the dependence of the sensor's sensitivity on the height of the ER structure, we performed PET tip scanning measurements on the tactile sensor using SKIN #3, with ridges of 25 μm, 50 μm, 75 μm, and 100 μm in height. The *f* ER of a pronounced peak appeared in the FFT results of all four tactile sensors at 125 Hz, corresponding to the scanning speed of 2.5 cm/s. Figure 7a shows the magnitude of *f* ER of all four tactile sensors with different ER heights. The magnitude of *f* ER increased by about 3.0 dB as the ER height increased from 25 μm to 75 μm, demonstrating that the increased height of the ER had an amplifying effect for shear sensing due to the increased torque. However, the magnitude of

*f* ER was greatly decreased at 100 μm in ER height. It seems that there was a limit to how high the ER could be made to take advantage of the amplifying effect, since beyond a critical ER height, or aspect ratio, we believe the ER structures may buckle and absorb the shear forces, leading to reduced force transfer. This height-dependent degradation has also been observed in other reports [30].

**Figure 6.** FFT results of tactile sensors with four types of SKINs when a PET tip was scanned at 2.5 cm/s on the tactile sensor. The indicated magnitude values show the magnitude of peak frequency (*f* ER) induced by a 200-μm period of ERs and a 2.5 cm/s scanning speed.

**Figure 7.** Dependence of the magnitude of *f* ER on (**a**) the height (*h*) of the ER and (**b**) the thickness (*t*) of the epidermal board.

We also investigated the relationship between the tactile sensitivity and the thickness of the epidermal board. We used the same material configuration of SKIN #3 at a scanning speed of 2.5 cm/s, but with different board thicknesses varying between 25, 50, and 75 μm, and kept the height of the ER at 50 μm. We found that the highest magnitude of *f* ER in the tactile sensor was from the 50-μm-thick epidermal board. This indicates that there may be an optimum thickness for the epidermal board that will give the highest force transfer while reducing the dispersion of the vertical force [31]. Due to these results, we chose SKIN #3 with an ER height of 75 μm and an epidermal board 50 μm thick for the following texture sensing.

#### **5. Surface Period Detection**

We evaluated the surface period-sensing characteristics of the developed tactile sensor using two periodic grating structures with a 150-μm and 625-μm period (insets of Figure 8a,b). The periodic grating structures were fabricated by a patterned 60-μm-thick SU-8. Figure 8a,b shows the FFT results of tactile sensor output induced by scanning performed at 1–4 mm/s. When the grating structure slid on the tactile sensor, individual ERs periodically interacted with each grating structure, and simultaneously the individual grating structure periodically interacted with each ER. Therefore, the FFT results present the *f* ER and the peak induced by a surface period of a contact object. We observed that the distribution of the peaks showed a blue shift as the scanning speed increased, with the scanning speed divided by the highest peak frequency remaining the same. At a 2-mm/s scanning speed (Figure 3a), a distinct peak for *f* ER was observed at 10 Hz, and peaks at 13.3 Hz were induced by the 150-μm period of the surface grating structure, with harmonic signals appearing at each 10- and 13.3-Hz multiple. We saw similar characteristics for the 625-μm periodic structure. This showed that our sensor was able to detect surface structures under 100 μm (75 μm wide and 60 μm high). Since the highest peak would come from the ER's interaction with the contacting surface, the *f* ER offered information on scanning speed. Then the other peaks in the spectra could elucidate the surface periods of the object with the known scanning speed.

**Figure 8.** Surface period-detecting characteristics of biomimetic tactile sensor: the FFT results of the tactile sensor output induced by scanning the grating structures with (**a**) a 150-μm and (**b**) 625-μm period at 1–4 mm/s scanning speed, where the insets show cross-sectional SEM images of the grating structures (scale bars indicate 200 μm); (**c**) the FFT results of the tactile sensor output induced by scanning a 3D-printed structure at 2 mm/s (varying the contact depth), where the inset shows a cross-sectional SEM image of the 3D-printed structure (scale bar indicates 500 μm).

In sensing the surface roughness, varying the contact depth was an important factor in measuring the surface structure with different heights. A plastic (polylactic acid) surface with a 390-μm-period elliptic structure with a 20-μm height difference was fabricated by using a fused filament fabrication-type 3D printer, as seen in the inset of Figure 8c. The reciprocating direction of the printing nozzle resulted in the height difference. Figure 8c shows the FFT results of the tactile sensor when the printing structure was scanned at 2 mm/s, increasing the contact depth. Comparing the FFT result at a 90-μm contact depth to that at 20 μm, we observed an additional peak at 5.12 Hz, reflecting the period of the printing structure with a lower height. The peak at 5.12 Hz was not considered to be a harmonic of 2.56 Hz due to its higher magnitude. This result shows that our tactile sensor was able to detect not only the surface period but also a 20-μm height difference in surface structure. In the deeper contact depth of 160 μm, the signal magnitude of FFT decreased. It seems that the ER was not able to deform and recover fast enough due to the increased pressure resulting from the increased contact depth.

When the SKIN interacts with the object's surface, the generated vibration should depend on the frictional aspect of the materials involved. Therefore, even with the same surface roughness, if the material is different, it should produce vibrations with different spectral distributions. We fabricated a PDMS replica of a finger, which should have had identical surface structures to the actual finger used, and measured the sensor signal through tactile scanning to see how the measured signals would differ. Figure 9a,b shows the FFT results, and it is clear that the two results were quite different and distinguishable. In both cases, distinct peaks were observed identically at *f* ER = 10 Hz (representing the ER period) and at 4.2 Hz (which was dependent on the 480-μm period of the human fingerprint). However, differences in the amplitudes of major peaks and their harmonics and spectral distribution in the lower frequency region were observed. This difference seems to have been caused by the stiffness difference between the human finger and the PDMS replica. The human finger showed larger deformability due to its Young's modulus (~100 kPa [25]) being lower than PDMS (750 kPa). The human skin's lower modulus led to a larger contact area and higher friction with the SKIN layer of the sensor. This allowed the characteristic peaks to become distributed, forming a higher amplitude spectrum in the low-frequency region. In addition, the chemical characteristics of a finger surface (cell membrane, oil, and sweat) differ from those of PDMS, which produces differing frictional characteristics. This result showed that our sensor was able to distinguish between different materials even if they had the same surface structure.

**Figure 9.** FFT results of tactile sensor output induced by scanning (**a**) a human finger and (**b**) a PDMS replica, where the insets show optical images of a human finger pad and the PDMS replica. Blue arrows indicate the *f* ER, and green arrows indicate the peaks of ERs of the contact objects.

#### **6. Texture Detection**

The ability to discern surface feature periods and contact materials may be used to distinguish the characteristics of similar materials. We performed surface scanning measurements on similar materials with different compositions to test this ability of our biomimetic tactile sensor. First, we compared the scanning results between a sheet of printing paper and a sheet of papyrus at a 2 mm/s scanning speed (see Figure 10a,b). The results taken from the printing paper scanning showed that the peaks at *f* ER and its integral multiples showed far lower amplitudes than did papyrus. These complex spectra could be simply analyzed using the energy spectrum density (ESD), which is the integral of FFT in a specific frequency range. Comparing the two results in the frequency range under and over *f* ER, the ESDs of the papyrus were 22 times and 13 times larger than those of the printing paper. It can be said that the papyrus had more surface features with periods shorter and longer than 200 μm. This can be verified visually (as shown in the optical and SEM images of Figure 10a,b), as the printing paper was featureless while the papyrus was feature-rich with many observable line structures. The surface features of the papyrus had higher directionality, periodicity, and height than did the printing paper. Due to these structural differences in surface topology, papyrus had the higher peak amplitude over most of the observed frequency range. Since our fingertips felt that the papyrus surface was "rougher" than that of the printing paper, the higher peaks in the FFT results may reflect the roughness of the material surface.

**Figure 10.** FFT results of tactile sensor output induced by scanning (at a 2-mm/s scanning speed) (**a**) a sheet of papyrus, (**b**) a sheet of printing paper, (**c**) cattle leather, and (**d**) polyurethane artificial leather, where insets show SEM and optical images of each contact object (scale bars indicate 500 μm).

For real leather and artificial leather, it is quite difficult for an untrained person to distinguish between the two just by scanning their fingers over the surfaces. We found that the scanning results of these materials did produce FFT results that were discernable. As shown in the inset images of Figure 10c,d, cattle leather and polyurethane leather have similar surface structures with many grains and pores. The FFT results showed a similar spectral distribution, which made it understandable why it would be difficult to distinguish between them just by relying on touch. However, there were features that stood out. In the frequency range from 8 to 22 Hz, the ESD of the cattle leather was 2 times larger than that of the polyurethane leather. This shows that our sensor may be used to detect fine differences between artificial and real leather.

The results of Figure 8c show that the contact depth was an essential factor in detecting more detailed surface characteristics. Varying the contact depth, we measured the change in the frequency spectrum between two fabrics with different hardness. As seen in the optical image of Figure 11a,b, the harder fabric had tight weaves and the softer fabric had hair-like structures. The topmost spectrum was recorded when the 10-Hz fingerprint peaks started to appear. Then the fabrics were scanned with the sensor pressed on its surface with the pressure depth increasing in 20-μm steps toward a 200-μm total depth. In the low-contact depth, we observed similar frequency spectra, which showed no specific peak except for *f* ER despite the significant visual differences between the two fabrics. Increasing the contact depth, we observed the appearance of peaks at 1.6 Hz and 0.2 Hz and their harmonics with a 1.2-mm period of the hard fabric and a 10-mm period of the soft fabric. Comparing the two FFT results, the harder-fabric FFT showed more distinct peaks appearing as the scanning pressure increased to that of the softer-fabric FFT. We can conclude that the frequency spectra taken at various contact depths can give additional hardness information, which will aid in increasing the ability of the biomimetic tactile sensor to distinguish between various materials and which may make tactile sensor-based material identification a reality.

**Figure 11.** FFT results of tactile sensor output induced by scanning (**a**) a hard fabric with tight weaves and (**b**) a soft fabric with hair-like structures, increasing the contact depth. Inset images show SEM and optical images of the two different fabrics (scale bars indicate 1 mm).

#### **7. Discussion**

Previously reported tactile sensors that were based on measuring scanning vibrations commonly used accelerometers and microphones [32,33]. These sensors were built into or on top of an artificial finger, and vibration damping could occur, which would reduce the accuracy of the results. Since our biomimetic tactile sensor would be in direct contact with an object, it could effectively detect the vibration information induced by the interaction between the ERs and the contact object without damping. Our SKIN showed tactile sensitivity that was high enough to detect sub-100-μm surface structures of a sheet of papyrus. It has been reported that ERs show good tactile sensitivity on surfaces with structural periods that are 0.5–2 times as long as that of the ERs [26]. When varying the design of the ER period, the tactile sensitivity may be adjusted to the target sensitivity, which may be required for various applications.

Conventional fingerprint identification security systems can detect the surface structure of human finger pads but cannot discriminate between finger pad replicas. In the results in Figure 9, it is shown that our biomimetic tactile sensor had the capability of discriminating between a human finger pad and its replica, which may make them applicable for future security systems. By utilizing its mechanical flexibility, if we incorporate the biomimetic tactile sensor into the previously reported tactile stiffness sensor [34], it may be possible to develop a portable tactile measurement system that can detect surface roughness as well as stiffness, which will make tactile surface recognition possible.

#### **8. Conclusions**

We developed a SKIN with a bilayer structure with different elastic moduli that mimics human ER and epidermis characteristics. When a hard ER and a soft epidermal board were used for the SKIN, we improved the effectiveness of transferring surface shear forces into vertical vibrations. The SKIN with a hard SU-8 ER and a soft PDMS epidermal board showed 6.85 times higher tactile sensitivity than did the one fabricated using only the soft PDMS. We optimized the ER dimensions and the epidermal board thickness, and the biomimetic tactile sensor with the optimized SKIN was able to detect a 75-μm period and a 20-μm height difference in a contact surface. Our sensor also showed an ability to sense the difference between contact with a human finger and a PDMS replica with the same surface structure. In addition, we demonstrated that it was possible to discern the differences in textures of paper, leather, and fabric using the biomimetic tactile sensor. With further development and through use of the sensor's mechanical flexibility, our biomimetic tactile sensor may make texture-recognizing artificial fingers a possibility.

**Author Contributions:** conceptualization, E.C.; formal analysis, E.C., O.S., H.S., and S.-B.L.; funding acquisition, S.-B.L.; investigation, S.K., S.Y., G.R., and Y.S.; methodology, E.C., O.S., and S.-B.L.; project administration, S.-B.L.; resources, J.L., Y.S.; supervision, S.-B.L.; visualization, H.Y. and O.S; writing—original draft, E.C.; writing—review and editing, S.-B.L.

**Funding:** This research was funded by the Basic Science Research Program through the National Research Foundation of Korea (NRF) (funded by the Ministry of Education, Science, and Technology (2012R1A6A1029029) and the Ministry of Education (2019R1I1A1A01059713)) and partly by the Samsung Research Funding Center of Samsung Electronics under Project Number SRFC-IT1701-03.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).


More recently, NO3 − production and accumulation of the nitrogen cycle have been widely observed and confirmed on Angkor sandstone monuments under the tropical climate in Cambodia [32,85]. The concentration of NO3 − is positively correlated with the acidity and pH value, among the different environmental variables of these sandstone monuments. The constant acidification on the cultural heritage initiates and accelerates the dissolution of mineral constituents in the sandstone and produces the phenomenon of salt attack by crystallization/efflorescence on/in the substratum material which leads to structural weakening and crack formation of the cultural heritage [16,29,30,35,70,93]. After successive sampling over a 3-year period and analyses of the ammonia-oxidizing archaea (AOA) and bacteria (AOB) by both genomic DNA and RNA from samples for real-time PCR, AOA was apparently higher than AOB on these sandstone monuments and detection of NO3 − was successful [32,85]. Furthermore, a stable isotope tracer experiment by addition of the 15N in the chemical forms of NH4 + or NO3 − was initiated and incubated with samples collected from several Angkor monuments to quantify the nitrogen transformation rate from nitrate removal as nitrogen gas and determine the contribution of the microbial community to the removal of available nitrate. The results of this study revealed that the heterotrophic denitrification (NO3 −→NO2 −→NO→N2) activity in the microbiome of the sandstone monument was low, and the activity of anammox (NH4 ++NO2 −→N2) in the sample from the monument was negligible [84]. According to data in this study, the nitrate removal reaction of denitrification was active, but not strong enough to remove the produced and accumulated NO3 − because ammonia oxidation is aerobic while denitrification is anoxic. The physical conditions of available oxygen most likely play the regulation role, resulting in the accumulation of nitrate on the Angkorian sandstone.

Sulfur is also involved in the sandstone attack through microbial transformation of S at different valent states. *Fusarium* sp. and *Mycobacterium* spp. were isolated from decayed materials of the sandstone monuments and elucidated their metabolic capability of sulfur oxidation to acidic sulphate via chemolithoautotrophic metabolism in laboratory experiments [61,62]. The metabolic products of these sulfur-oxidizing bacteria are capable of producing inorganic acid through oxidation of S and the acidity in the form of H+ interacts with minerals causing dissolution and re-crystallization regulated through water retention and loss by natural force, resulting in structural weakening of the stone material integrity and disintegration [29,40,70]. These findings suggested that these sulfur-oxidizing *Fusarium* sp. and *Mycobacterium* spp. participate in the deterioration process (i.e., acidification) of the sandstone monuments under tropical conditions. The accumulation of acids, NO3 − and SO4 2−, which are corrosive to the inorganic material substratum of the cultural heritage, can be observed by microbially-mediated biochemical transformations.

As the former section stated, NGS techniques can provide thorough information of the microbial community and composition inhabiting cultural heritage to allow for identifying of the microorganisms without culturing them, which cannot be achieved by a culture-dependent approach. However, the involvement of the new microorganisms in the biodeterioration needs further careful verification even if high percentages of sequence reads with a metabolic potential are detected. The results of NGS-based investigations can provide useful information for the purpose-driven biochemical functional analysis and identify the physiological characteristics of the abundant genes and associated groups for developing culturing strategies to enrich and isolate the implicated members for further verification of their biochemical and ecophysiological roles in deterioration (Figure 4). Therefore, NGS technology can be employed to obtain basic information to assist in further research directions and strategies to directly confirm the scientific questions prior to culture-based strategies or activity-based biochemical functional analysis. It is also important to use NGS to gain an overview or summary of the whole microbial community prior to a genuine scientific question being executed. In doing this way, culture-dependent and -independent methods can be integrated effectively to advance scientific research on cultural heritage and also provide useful understanding for protection and management.

### **5. Summary and Future Perspectives**

Current research addressing the issue of biodeterioration of cultural heritage needs to focus on sampling methods and appropriate analytical techniques used afterward. Application of non-invasive/non-destructive sampling strategies is required in microbiological studies for conservation. The NGS-based metagenomics and metatransriptomics should be applied to the study of biodeterioration of cultural heritage for the microbial structure (especially spatial distribution structure) of the microbiome and the potential of specific microbial groups and microbe-mediated transformation processes contributing to biodeterioration. The NGS-based results can provide sound basis for the purpose-driven biochemical functional analysis or develop enrichment and culturing strategies to isolate the implicated groups of microorganisms with significant roles in biodeterioration. There are apparent challenges for investigations to develop the purpose-specific biochemical functional analysis for measuring the valid deterioration effects of microbiome and for enhancing knowledge of cultivation of microorganisms from a specific material.

Delineation of the processes and mechanisms involved for (bio)deterioration should be the basis for understanding the current status with a selective cultural heritage. When the destructive reactions are known and also the specific microorganisms responsible for the processes, remediation strategies and management plan can be proposed accordingly [94]. Sulfur-driving acid production can be dealt with from restriction on fuel quality and air quality mandate in policy and management while intervention on site can be carried out through mineralization to immobilize produced SO4 2−. NO3 − is an issue that is more difficult to address than SO4 2−, but an effective way to deal with it should be to focus on the source of NH4 + to eliminate its contribution from the beginning. In addition, new emerging science on materials can also take an important role in the effective protection and management of culture heritage to decrease the detrimental contribution from microorganisms and their biochemical capabilities. Overall, protection of cultural heritage is a complex subject and synergies of different subjects will contribute to the advancement of this field for both basic knowledge and also applications.

**Author Contributions:** Conceptualization, W.L. and J.-D.G.; writing—original draft preparation, X.D.; writing review and editing, X.D., W.L. and J.-D.G.; supervision, J.-D.G.; project administration and funding acquisition, W.L. and J.-D.G. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by Shenzhen Scientific Project Fund (JCYJ20170410103015603), and Hong Kong RGC GRF Grant (No. 17302119).

**Acknowledgments:** This project was financially supported by Shenzhen Scientific Project Fund (JCYJ20170410103015603), and Hong Kong RGC GRF Grant (No. 17302119). We would like to thank the Apsara Authority of the Kingdom of Cambodia for permission to conduct sampling and research for more than a decade, Ka Hong Cheung for the SEM in Figure 1, and Lin Gao for help with the photographs presented in Figure 2.

**Conflicts of Interest:** The authors declare no conflict of interest.

### **References**

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*Article* **Flexible Tactile Sensor Array for Slippage and Grooved Surface Recognition in Sliding Movement**

**Yancheng Wang 1,2,\*, Jianing Chen 2 and Deqing Mei 1,2**

Received: 16 July 2019; Accepted: 28 August 2019; Published: 30 August 2019

**Abstract:** Flexible tactile sensor with contact force sensing and surface texture recognition abilities is crucial for robotic dexterous grasping and manipulation in daily usage. Different from force sensing, surface texture discrimination is more challenging in the development of tactile sensors because of limited discriminative information. This paper presents a novel method using the finite element modeling (FEM) and phase delay algorithm to investigate the flexible tactile sensor array for slippage and grooved surfaces discrimination when sliding over an object. For FEM modeling, a 3 × 3 tactile sensor array with a multi-layer structure is utilized. For sensor array sliding over a plate surface, the initial slippage occurrence can be identified by sudden changes in normal forces based on wavelet transform analysis. For the sensor array sliding over pre-defined grooved surfaces, an algorithm based on phase delay between different sensing units is established and then utilized to discriminate between periodic roughness and the inclined angle of the grooved surfaces. Results show that the proposed tactile sensor array and surface texture recognition method is anticipated to be useful in applications involving human-robotic interactions.

**Keywords:** finite element modeling; surface texture; grooved surface; tactile sensor array; wavelet transform; spectral analysis; inclined angle

#### **1. Introduction**

Flexible tactile sensors have been widely utilized in robotics, prosthetic hands, and medical surgery [1,2]. For grasping and manipulation tasks, the robotic hand with integrated tactile sensors can perceive tactile information between the hand, fingers, and grasped objects. This tactile information plays an important role and can be used for robotic feedback control [3,4]. For daily grasping in robotic and prosthetic hands, if the applied grasping force is too low, objects may slip through the hand, while fragile objects may be damaged when the applied force is too large. Furthermore, the roughness, texture, material hardness, and contour of the objects also affect the requisite grasping force [5]. Therefore, robotic dexterous manipulation generally requires integrated tactile sensors on the robotic hand with force sensing as well as object texture and contour shape recognition abilities.

The tactile sensor array is usually designed with several sensing units arranged in a row/column configuration and can be used to measure distributed contact forces [6,7]. In the past decade, developments in the tactile sensor array have attracted many researchers, and several types of tactile sensor array have been proposed [8–10]. Recently, we utilized conductive rubber as the sensing material to develop a flexible tactile sensor array with 3 × 3 sensing units which can be worn on the finger of a robotic hand and can measure three-axis contact forces during grasping applications [11,12]. The contact behavior of the sensor array with objects affects the contact force sensing performance of the tactile sensor array we developed. Analytical modeling was conducted to study the sensing

performance and mechanical behavior in many researches. The basic structure of the tactile sensor is usually first simplified into a combination of cylinders, cuboids and other basic geometries. Then a lumped parameter model can be developed to analyze the mechanical properties of the tactile sensor. Zhang et al. [13] utilized the Stribeck friction model to study the mechanical behavior between a rigid gripper and the gripped object during the initial slippage phase. They found that the induced normal force changes suddenly when slipping occurs due to change in the static/dynamic friction coefficient. Ho et al. [14,15] simplified the fingertip into a bundle of beam to calculate localized displacement for slip detection.

For a tactile sensor array with a more complex structural design, numerical modeling will be an effective approach to study the performance and contact behavior of the tactile sensors. Dao et al. [16] presented a numerical model in Marc Mentat software that analyzed the normal stress distribution of the sensing units when an external force is applied and identified the optimal location for the arrangement of piezoresistors. Youssefian et al. [17] developed a finite element modeling (FEM) of the tactile sensor by adopting nonlinear elastic material properties to study the induced stress and strain when a normal force is applied to the outer surface of the tactile sensors. In these proposed numerical models, the structure of the tactile sensors needs to be simplified into beam and plate structures for fast calculation convergence. Therefore, this will inevitably affect the accuracy of the mechanical behaviors of the tactile sensors for external force sensing, like the filtering effects of the sensor's top cover material is neglected [18]. Thus, to analyze the sensing performance and contact behavior, an accurate 3D FEM model of the tactile sensor array needs to be developed, and this is a goal of this research.

As mentioned earlier, the surface roughness, hardness, texture and contour shape of the object affect the sensing performance of the tactile sensor array. For surface texture recognition, two approaches have been validated to have the ability to extract the object's features. (1) Using a tactile sensor array with high-density sensing units to measure the contact forces when it touches the object's surface. Then the measured force values are plotted into a gray scale figure, which can be used to discriminate between the contour shapes of the objects using an image processing algorithm [19,20]. (2) Using a spectral analysis algorithm to analyze the measured forces when the tactile sensor slides along the surface of the objects. Oddo et al. [21] utilized a 2 × 2 tactile sensor array to measure the normal forces when sliding over the patterned surfaces, and fast Fourier transformation (FFT) was used to discriminate between the surface roughness and periodic information from the grooved surfaces. Further, they developed an approach using a machine learning algorithm (k-NN classifier) and wavelet transform to classify the surface texture [22]. In 2012, Fishel et al. [23] utilized a biologically inspired tactile sensor (BioTac) to measure tactile vibrations and reaction forces when exploring surfaces with different textures. The Bayesian exploration algorithm was then used to analyze the force data obtained, and 117 types of textures were successfully identified. This algorithm requires plenty of input data for the improvement of accuracy, and this limits the application of the BioTac sensor for surface texture recognition. Therefore, based on the obtained reaction forces of the tactile sensor array, an effective surface texture recognition method still needs to be developed.

Therefore, the proposed tactile sensor for surface texture recognition still needs to be investigated, the present surface recognition method can still not be used for practical usage. To fill this research gap, we developed an accurate 3D FEM model of the tactile sensor array to study the sensing performance and contact behavior of the sensor when contacted with objects. Based on the measured contact forces of the tactile sensor array, a novel approach based on phase delay algorithm for grooved surface recognition is developed and verified by both FEM modeling and experimental validation. The main content of this paper is divided as follow: Section 2 presents the structure and working principles of the tactile sensor array on which 3D FEM modeling was conducted. Section 3 describes the experimental setup and procedures. Two sets of experiments were conducted: slippage detection and surface texture recognition when the sensor array slides over the object's surface. The FEM simulation, experimental results, and discussion are presented in Section 4.

#### **2. Design of Tactile Sensor Array and FEM Modeling**

#### *2.1. Flexible Tactile Sensor Array*

Of the tactile sensing principles, piezoresistive sensing is selected because of its relatively simple structural design and good anti-noise performance. Highly sensitive INASTAMOR pressure conductive rubber (from Inaba Rubber Co. Ltd., Osaka, Japan) is utilized as the sensitive material and cut into small pieces of round-shaped chips with a diameter of 3.0 mm. The structural design of the 3 × 3 flexible tactile sensor array is illustrated in Figure 1a,b. This sensor array mainly consists of three layers: top polydimethylsiloxane (PDMS) bump, a middle room temperature vulcanizable (RTV) adhesive layer with conductive rubber chips, and bottom electrodes on a thin film of polyethylene terephthalate (PET). The thicknesses of these three layers are 0.8, 0.5 and 0.1 mm, respectively. The distance between adjacent units is about 3.5 mm, and thus the overall dimensions of the tactile sensor array are 20 mm × 16 mm × 1.4 mm. A detailed structural design of the flexible tactile sensor array can be found in one of the references [12].

**Figure 1.** (**a**,**b**) Structure of the flexible tactile sensor array; (**c**) Fabricated tactile sensor array.

The patterned electrodes underneath the rubber chip have four side electrodes and one central common electrode, which generates four resistors (*R*1, *R*2, *R*3 and *R*4) and divides the sensing unit into five areas, as shown in Figure 1b. Thus, these four resistors can measure the changes in resistance for external three-axis force sensing. Typically, as the tactile sensor array is worn on the finger of a hand for grasping and touching objects, the external force will be exerted over the sensor array, and the induced deformation of the PDMS bump and conductive rubber chips will change the resistances of these four resistors.

#### *2.2. FEM Modeling*

For FEM modeling, the finite element mesh of the 3D tactile sensor array model with dimensions of 20 mm × 16 mm × 1.4 mm is shown in Figure 2a. The accurate 3D geometry of the bump, rubber chip, and substrate film layers were converted to the FEM mesh using ABAQUS (v6.14, Dassault Systèmes Simulia Corp., Providence, RI, USA). Both "structured" and "sweep" algorithms for element mesh were utilized. The bump layer, rubber chip and surrounding RTV adhesive, and the PET film were connected using the "tie" function to lock the nodes onto the surfaces. To ensure perfectly tied surfaces, the mesh (node positions) on the mating surface must be consistent. For mesh convergence, the region of the bump and rubber chip and other contact regions were finely meshed, as shown in Figure 2b. Altogether, the tactile sensor array was meshed using 78,885 eight-node hexahedron elements. The element numbers in each layer are as follows: bump (53,236 elements), conductive rubber chip (10,368 elements), RTV adhesive (14,001 elements) and bottom PET film (1280 elements). For boundary conditions, the PDMS bump, conductive rubber chip, RTV adhesive, and PET film layers were merged together. The underside of the PET layer was confined by the displacement boundary condition.

**Figure 2.** (**a**) 3D finite element modeling (FEM) model of flexible tactile sensor array; (**b**) Close-up view of sensing unit.

The mechanical properties of the PET film were adopted from a previous study while Young's modulus and Poisson ratio are about 3000 MPa and 0.47 [24]. For the conductive rubber, PDMS and RTV adhesive, uniaxial compression tests were conducted according to American Society for Testing and Materials (ASTM) standards [25]. The measured nominal stress versus nominal strain curves for the rubber, PDMS and RTV adhesive materials are shown in Figure 3.

**Figure 3.** Measured nominal stress versus strain curves of rubber, room temperature vulcanizable (RTV) and polydimethylsiloxane (PDMS) materials.

All three stress-strain curves have nonlinear elastic behaviors, especially under large strains. The hyper-elastic Yeoh model [26] was used in the ABAQUS software to represent the nonlinear properties of the conductive rubber, PDMS and RTV adhesive materials. For these incompressible materials, the strain energy density function can be expressed as

$$\mathcal{W}(I\_1) = \mathbb{C}\_1(I\_1 - 3) + \mathbb{C}\_2(I\_1 - 3)^2 + \mathbb{C}\_3(I\_1 - 3)^3 \tag{1}$$

where *I*1 stands for the first invariant of the Green deformation tensor and *Ci* is the material parameter. Under the circumstance of uniaxial compression, Equation (1) can be transformed into the form that describes the relation between the stress σ and strain ε as

$$\begin{aligned} \sigma &= 2\mathbb{C}\_1(\lambda - \lambda^{-2}) + 4\mathbb{C}\_2(\lambda^3 - 3\lambda + 1 + 3\lambda^{-2} - 2\lambda^{-3}) \\ &+ 6\mathbb{C}\_3(\lambda^5 - 6\lambda^3 + 3\lambda^2 + 9\lambda - 6 - 9\lambda^{-2} + 12\lambda^{-3} - 4\lambda^{-4}) \end{aligned} \tag{2}$$

where λ is the elongation and equals to 1 + ε.

By using the least squares fitting, three parameters (*C*1, *C*2, and *C*3) in the Yeoh model for rubber, RTV adhesive, and PDMS are obtained, and these are listed in Table 1.

**Table 1.** Material properties of rubber, RTV, and PDMS materials.

#### **3. Experimental Setup and Procedure**

#### *3.1. Experimental Setup*

The entire experimental setup is shown in Figure 4. It mainly consists of an *xyz* linear motion stage and a three-axis commercialized force sensor. The developed flexible tactile sensor array was attached to a plastic loading bar, which was mounted to a *z*-axis motion stage. A scanning circuit based on digital signal processing (TMS320F2812, Texas Instruments Inc, Dallas, TX, USA) was designed and used for the distributed normal and shear forces measurement [12]. During the experiments, two types of surfaces (flat surface and grooved surface) made using stereolithography (SLA) technique were utilized. The flat surface was used for slippage detection when the loading bar and tactile sensor array slid on the surface. The grooved surfaces with different patterns were used for surface texture recognition.

**Figure 4.** Experimental setup for the validation tests.

#### *3.2. Experimental Procedure*

During the experiments, the motion speed of the linear stage was controlled by the stepping motors. To reduce the inertia effects, the sliding movement of the loading bar and tactile sensor array should be lower than 1.0 mm/s. For experimental validation, two sets of experiments were conducted:

*Slippage detection.* First, the loading bar with a tactile sensor array compressed the flat surface for 8 s, and the induced compression force was increased up to 20 N. This force was a little large than that of FEM simulation, because this force is sufficient to overcome the effect of the death zone, and make the output voltage of each sensing unit clear enough (over 0.5 V) for further analyses. Secondly, this is followed by a holding stage that lasted for 15 s. Thirdly, the loading bar and tactile sensor array slid along the plate surface for about 25 s at a constant speed of 0.25 mm/s.

*Surface texture recognition.* Three grooved surfaces with different spatial periods of 0.9, 1.2, and 1.5 mm were utilized. The inclined angle (α), defined as the angle between the grooves and the *y*-axis, was set as 0–60◦ with an increment of 15◦. The tactile sensor array first compressed the grooved surface for about 3 s, and then held for 10 s. The induced compression force was also kept as 20 N. Then, the loading bar and tactile sensor array slid along the grooved surfaces for about 40 s.

The sampling rate of the scanning circuit for the generated forces sensing was set as 0.1 kHz. For each experimental set, three repeated tests were carried out repeatability.

#### **4. Results and Discussion**

#### *4.1. Slippage Detection in Sliding Movement*

By using the developed FEM model, the sliding movement of the tactile sensor array over the flat surface was analyzed. During the simulation, the sliding movement is divided into two steps. Step I—compressing and holding: the tactile sensor array was vertically compressed against the flat surface until the total reaction force reaches up to about 15 N. This force was a little lower than that of experimental tests with the aim to improve the convergence property and the convergence effectiveness of sliding movement during FEM simulation. As for each sensing unit, the force difference between experimental and FEM simulation can be further decreased to about 0.56 N. Then this step was held for 1 s to maintain the normal force of 15 N. Step II—sliding: the sensor array was moved sliding along the *x*-axis direction at a speed of 1.0 mm/s. Because our developed tactile sensor array has extremely low weight less than 10 g, the moving speed lower than 1.0 mm/s can be considered as the quasi-static state movement. So, the moving speed lower than 1.0 mm/s has little effect on the FEM simulation predicted forces and will greatly reduce the simulation time.

FEM simulation results for each sensing unit at the end of the compression and sliding stages are shown in Figure 5. In Figure 5a,c, the generated normal stress along the *z*-axis is symmetrically distributed at the cross-section view of the sensing unit during the compression stage. During the sliding stage, the compression stress in the left region (marked as *L*) is generally increased and becomes greater than that in the right region (marked as *R*), as shown in Figure 5b,d. This phenomenon has been confirmed by other studies [11,12] and can be attributed to the torque caused by the friction between the flat plate and the PDMS bump. The shear deformation of the sensing unit also occurred during sliding movement. This can be explained as the localized displacement occurred at the contact region of the PDMS bump [14,15]. The lower boundary of the hemisphere-shaped bump cannot immediately follow the movement of the upper part of the tactile sensor due to the friction in the contact region. Thus, gross slippage will be generated as the distance between the contact region and the PET substrate is stretched long enough to overcome the effects of friction.

**Figure 5.** The stress distribution in the cross-section view of the sensing unit at the end of (**a**) compress and (**b**) sliding. A-A cross-section at the end of (**c**) compress and (**d**) sliding.

The normal force generated at the left and right areas of the sensing unit are extracted based on the simulation results, as shown in Figure 6a. In Figure 6a, the sliding direction of the tactile sensor array along the flat surface can be observed as from the red area to the blue area. The normal forces obtained at both the left and right area are gradually increased from zero to 0.07 N at the loading stage. During the sliding stage, the normal force at the left area is increased significantly, while the generated normal force in the right area is decreased. Though the force's amplitude was generally small, the relative change rate almost reaches 100%. Therefore, the sliding direction can be identified based on the obtained normal force curves in the left and right areas.

**Figure 6.** (**a**) Simulated normal force extracted from the left and right area of the patterned electrodes in the sensing unit (**b**) Measured voltages of *R*1 and *R*3 when sliding along a flat surface, DSWT analysis of the simulated normal force in left area (**c**) and right area (**e**) DSWT analysis of the measured voltages in *R*1 (**d**) and *R*3 (**f**).

To validate the FEM prediction, experimental tests are conducted. Figure 6b shows the measured voltages of the tactile sensor array in *R*1 and *R*3 resistors when the sensor array is compressing and sliding on a flat surface. The experimental setup and procedure adopted are presented in the preceding Section 3. Generally, the measured voltages of *R*1 and *R*3 have greater variations while having almost the same trends as that of the simulated normal force, as shown in Figure 6a. This is because the sensitivity of the utilized conductive rubber material in tactile sensor is over 500 kΩ /N when the applied force is lower than 0.7 N [12]. At the sliding stage, the measured voltage of *R*1 is also increased, and the voltage of *R*3 is decreased. Therefore, we can clearly distinguish the sliding occurrence and direction from either the FEM simulated normal forces or the measured voltages at the side electrode area and resistors.

Initial slippage detection is proven to be important for robotic hand grasping. Wavelet transform has been utilized to analyze the measured forces or voltages of the tactile sensors and demonstrates the ability to identify the change of the derivation for initial slippage discrimination [28]. Here, we also utilized the wavelet transform to analyze the simulated normal forces and measured voltages in Figure 6a,b. Figure 6c–f shows the discrete sequence wavelet transform (DSWT) results of the simulated normal forces and measured voltages of *R*1 and *R*3, respectively. We picked Coiflet as the mother wavelet function and set its length equal to 6. Two peaks can be observed at the transition moments from the loading to holding and from the holding to sliding stages, as shown in Figure 6c–f. The variations of the wavelet coefficient at initial sliding are much greater than that after loading. Thus, by setting a reasonable threshold value for the wavelet coefficient, the initial slippage can be distinguished as the tactile sensor array contacts and slides along object surfaces. More details of this method for slippage detection can be found in our previous study [11].

#### *4.2. Surface Texture Recognition*

#### 4.2.1. Phase Delay Algorithm for Surface Texture Recognition

Spectrum analysis of the tactile sensor's output voltages can be used to determine the spatial periodical information of the utilized grooved surfaces. The spatial period value (*D*), defined as the distance between two adjacent ridges (as shown in Figure 7b), can be calculated as:

$$D = \upsilon / \text{MAF} \tag{3}$$

where *v* is the sliding speed, and MAF stands for the frequency with the maximum amplitude.

**Figure 7.** (**a**) Force-time curves of No. 1–3 units, (**b**) schematic view for the calculation of inclined angle α.

In practical application, the grooved texture in the plate surface usually has an inclined angle of α. Applying FFT to the obtained data may get us a fake spatial period value equal to *D*/cos(α). Therefore, the inclined angle (α) also needs to be determined. For this purpose, we changed the distance between the sensing units in each row and column to create different phase delays, as shown in Figure 7b. We assumed that the force-time curves of one column of three sensing units are as shown in Figure 7a. The horizontal movement ends at *T*2, when No. 2 unit is at the center of the groove, which causes the force-time curve to end at a minimal value. At *T*1, the No. 3 unit will also be in a similar position where it sustains the lowest pressure. If the sliding movement continues, the force of No. 1 unit will drop to the same value at T3. In this condition, the movement path distances (AB and MO) can be calculated as *vt*1 and *vt*2, respectively, where *t*1 and *t*2 are the gaps between each moment, as indicated in Figure 7a. If the distance AC (*l*1) or CO (*l*2) is longer than that of *D*/sin(α), the inclined angle (α) can be calculated using the anti-trigonometric function and can be expressed as

$$\alpha = \arctan(\frac{\upsilon \cdot t\_1}{l\_1}) \tag{4}$$

The schematic diagram to calculate α is shown in Figure 7b. CGH is first created as the same as CBA, where point H is located on line CO. Then, we connect point G and point M and create a right triangle GNM, where line GN is perpendicular to line MN. The angle ∠MGN is the same as that of inclined angle α. The length of GN and MN can be calculated as (*l*1−*l*2) and *v*·(*t*1−*t*2), respectively. Thus, the inclined angle α can be calculated as

$$\alpha = \arctan[\frac{\upsilon \cdot (t\_1 - t\_2)}{l\_1 - l\_2}] \tag{5}$$

Also, the spatial period value (*D*) can be calculated as

$$D = \upsilon \cdot \cos(\alpha) / \text{MAF} \tag{6}$$

The procedure and flow chart of the phase delay algorithm for the grooved surface texture recognition is shown in Figure 8. The whole procedure can be mainly divided into three modules. In Slippage Judging module, the threshold based on wavelet coefficient is used. As the coefficient value is larger than 0.002, the program will jump out of the first loop and enter the Data Preprocessing module. The scanning circuit will sample the voltage data from three electrodes in the same column for 10 s. Using the low-pass filtering, the time gap (*t*1 and *t*2) is calculated based on the cross-correlation function analysis in this step. As for simulation, the force curves usually have an approximate sinusoidal shape. As for real tests, the measured voltage signals will be affected by external noises and vibrations, making the voltage curves not as good as that of the simulation results. Thus, we reconstructed the sine function of the characteristic frequency in the time domain and input these new curves into the cross-correlation function as shown in Figure 8. This step can be regarded as "band-pass filtering", which can improve the accuracy of the final results. After getting the reconstructed sine function, the program enters the Period Calculating module. By using Equations (5) and (6), the inclined angle (α) and spatial period value (*D*) can be calculated. Both maximum and minimum points of the cross-correlation curve will be taken as two different inputs. Therefore, we can obtain two spatial period results in the end of this module. If the difference between these two values is greater than 0.01 mm, the program will jump back and sample another set of data for a new round of calculation. Otherwise, the mean value will be output.

**Figure 8.** Flow chart and procedure to calculate the inclined angle and spatial period value for the grooved surfaces.

#### 4.2.2. Spatial Period Discrimination

To verify the ability of the developed method for surface texture recognition, both simulation, and experimental tests were performed when the tactile sensor array slid along the grooved surfaces. For grooved surface recognition, the compression force was set as 15 N and 20 N for FEM simulation and experimental tests, respectively. The applied force during experimental tests is a little larger. The reasons are the measured output voltage of our tactile sensor array usually contains some noises from the scanning circuit and environment. If the applied force is too small, it will affect the accuracy of tactile sensor for surface texture recognition. Even the misjudging of the frequency characteristics may be occurred, and leads to false result. For the utilized grooved surfaces, as shown in Figure 9a, the inclined angle of the grooves on the plate is set as zero and spatial period as 0.9, 1.2 and 1.5 mm,

respectively. As the sensor array slid along the surface, the measured voltages of one sensing unit for these grooved surfaces are shown in Figure 9b. We can see that the variation of voltage signals increases with the increase in the spatial period. For example, the grooved surface with 1.5 mm spatial period has the highest variation in the voltage curve. It is because narrower groove makes the compressed bumps of sensor less released.

**Figure 9.** (**a**) Three grooved surfaces with different spatial periods of 0.9, 1.2 and 1.5 mm; (**b**) Measured voltage; (**c**) Spectrum analysis for surface texture recognition.

Using the calculation procedure in Figure 8 (simplified, as there is no inclined angle), spectrum analysis is conducted, as shown in Figure 9c. For comparison, the MAF and spatial periods of the grooved surfaces in simulation and experimental tests are calculated and listed in Table 2. We can see that the deviation of the calculated MAF and the spatial period from the real values are generally low as the errors are usually smaller than 6.7%. Thus, we can conclude that plate surfaces with different

grooved textures can be recognized successfully using the method developed and the proposed tactile sensor array.

**Table 2.** Results of calculated spatial period and inclined angle in grooved surfaces.

4.2.3. Inclined Angle Calculation in Grooved Surfaces

To validate the developed method for the calculation of grooved surface with an inclined angle, we set *l*1 and *l*2 as 3.5 mm and 3.8 mm, respectively. For simulation and experimental tests, the grooved surface's spatial period is set as 1.2 mm and the inclined angle of the grooved patterns as 0◦, 15◦, 30◦, 45◦ and 60◦, respectively.

According to the calculation procedure and Equations (5) and (6), the inclined angle and spatial period for simulation and validation tests are calculated as shown in Table 2. We can see that both the calculated inclined angle and spatial period values for the simulation and experiments generally match well with the pre-determined results. For angle calculation, the greatest errors (23.3% for the simulation and 7.4% for the experiment) occur when the inclined angle equals 15◦. It is because the slope of the inversed tangent function is extremely steep when the inclined angle is lower than 20◦. Thus, even a small difference in the inputs would affect the accuracy of the calculated results. For the angle in the range of 30◦ to 60◦, the relative errors are greatly reduced and less than 5.0%, as shown in Table 2. For the spatial period calculation, as it is also influenced by the sampling time, the error's variation shows a different trend compared with the previous one. Still, the biggest error is less than 9.2% for both simulation and experiment tests. Therefore, a plate with inclined arranged grooves on the surface can also be recognized using the developed method.

Typically, the simulated force curves for the inclined angle of 0◦, 30◦ and 60◦ are shown in Figure 10a,c,e. The peak number in the force curve is decreased from 7 to 6 when the inclined angle increased from 0◦ to 30◦. The peak number dramatically drops to 3 when the angle is raised up to 60◦. This is due to the inclined grooves will enlarge the horizontal gap distance between the adjacent grooves. Under a uniform sliding motion, the larger angle will increase the time period, and in turn leads to less peaks, as show in Figure 10a,c,e. These three figures also show the trends that the overlapped force curves are gradually apart from each other. The green one is much closer to the red one than that of blue one. This phenomenon verifies that the existence of phase delay which caused by the inclined arranged grooves. Longer distance between elements 2 and 3 makes the phase delay more obviously. As in Figure 10e, the variation of the force curves drops from 0.08 N to 0.04 N with the increase of inclined angle. The curves stand for the total normal force applied on a fusiform area as shown in Figure 5. The inclined grooves arrangement will increase the minimum contacted area between the ridges and the fusiform area during sliding. Thus, the bump could not be fully released, and making the force curve variation become smaller.

**Figure 10.** Simulated normal force curves when the sensor array is sliding along the grooved surface with an inclined angle α equal to (**a**) 0◦, (**c**) 30◦ and (**e**) 60◦. Measured voltages from the validation test for inclined angle calculation when α equals (**b**) 0◦, (**d**) 30◦, (**f**) 60◦.

The measured voltage curves in real tests are shown in Figure 10b,d,f. Most phenomena discussed in the simulation cases could be verified here, like the peak number drops from 8 to 4 when the angle increases to 60◦. The above results indicated that the grooved surfaces with different spatial period and inclined angle arrangement could be successfully discriminated by using the proposed flexible tactile sensor sliding motion and phase delay algorithm, thus may have potential in robotic grasping tasks for surface texture recognition.

#### **5. Conclusions**

This study develops a methodology using FEM modeling and the Phase Delay Algorithm to validate the flexible tactile sensor array for slippage and surface texture recognition in sliding motions. The structure and working principle of the tactile sensor array and its 3D FEM modeling are presented. The hyper-elastic Yeoh model is utilized to describe the material properties of PDMS, RTV adhesive, and conductive rubber utilized in the tactile sensors. For the sensor array sliding along the flat surface, both FEM simulation and experiments demonstrated that slippage occurrence and sliding direction can be determined based on the simulated normal force and measured voltages in the side resistors' region. For surface texture recognition, the Phase Delay Algorithm and its calculation procedure are

developed. Results also showed that the grooved surface with and without inclined arranged grooves can be successfully discriminated.

This study opens up the opportunity to study surface texture identification for a flexible tactile sensor array in real applications. Optimal structural design of the flexible tactile sensor array including electrode pattern's design needs to be performed in future work. Further, the approach of using phase delay algorithm and artificial neural network for the developed tactile sensor array for robotic hand discrimination of unknown surface textures will also be conducted.

**Author Contributions:** Sensor design and modeling, Y.W.; FEM simulation and experimental tests, J.C.; conceptualization, D.M.

**Funding:** This work was supported by the National Natural Science Foundation of China (51575485), Zhejiang Provincial Funds for Distinguished Young Scientists of China (LR19E050001), and Creative Research Groups of National Natural Science Foundation of China (51821093).

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).


**7. Dendrimers for Target-Specific Delivery Applications**

Recently, numerous dendrimers were identified to be beneficial as potential targeted drug delivery systems for specific biomedical applications. Swanson et al. (2022) synthesized a novel fifth-generation PAMAM dendrimer, which was surface-modified and functionalized with folic acid. The remaining primary amines in the terminal dendrimer part were conjugated with the bifunctional 1, 4, 6, 10-tetraazacyclododecane-1, 4, 7, 10 tetraacetic acid, alpha-[5-isothiocyanato-2-methoxyphenyl]-, hydrochloride [9Cl] (NCS-DOTA) chelator, where stable gadolinium complexes were loaded into the dendrimer. The resultant dendrimer nanoparticle was about ~5 nm in size with the ability to be a potential target-specific magnetic resonance imaging (MRI) contrast agent with high affinity towards folate receptors in the liver and kidney for identifying human epithelial cancer cells in a murine model [120]. Similarly, Mbatha et al. (2019) prepared PAMAM dendrimer modified with folic acid and functionalized with nanosized gold particles for the efficient and targeted transport of exogenous small interfering ribose nucleic acid (siRNA). The study emphasized that the resultant dendrimer nanosized particles were spherical shaped with 65–128 nm of size and zeta potential above 25 mV, which indicates their high colloidal stability. Additionally, the dendrimer-based nanocomplex was less toxic to human embryo kidney (HEK-293) and HeLa-Tat-*Luc* cells (about 90% of cell viability). The study emphasized that the nanocomplex helped in the 75% increase in the induced transgene-silencing of siRNA and decrease the presence of excess folic acid for effective inhibition of hepatocellular carcinoma (HEPG2) and colon carcinoma (Caco-2) cells [121]. Likewise, Umeda et al. (2010) demonstrated the fabrication of polyethylene glycol (PEG) attached to a PAMAM dendrimer of four generations for the encapsulation of nanosized gold particles as shown in Figure 5. The results revealed that the nanosized gold particles were 2–3 nm in size with spherical morphology, which is engulfed by a single dendrimer particle, while the PEGylated dendrimer encapsulated gold nanoparticles were 15 nm in size. The study revealed that the dendrimers with gold nanoparticles possess enhanced toxicity against HeLa cells under the irradiation of visible light via photoinduced heat generation capability [122].

**Figure 5.** Growth of gold nanoparticles inside the PEGylated PAMAM dendrimers. Reproduced with permission from BUmeda et al. Ref. [122]. Copyright 2010 ACS.

Xiong et al. (2019) exhibited the formation of carboxybetaine acrylamide zwitterion functionalized fifth-generation PAMAM dendrimer and morphine, which is an agent to target lysosome and to entrap nanosized gold particles. The study showed that the nanosized gold particles entrapped by dendrimer are beneficial for the delivery of the H1C1 gene, enhanced by serum. The results showed that the dendrimer-entrapped nanosized gold particles were of size ~1.5 nm, where the vector system helped to carry the H1C1 protein for effective cancer cell migration and metastasis inhibition [123]. Further, Sharma et al. (2021) prepared novel hydroxyl terminated PAMAM dendrimers, that are modified with sugar moieties, such as alpha-D-mannose, beta-D-glucose, or beta-D-galactose via click chemistry approach. The resultant dendrimers were ~4 nm of average particle size with nearly neutral zeta potential and are utilized to target tumor-associated macrophages and microglia. The study showed that the glucose-modified dendrimer has increased brain penetration and cellular internalization, compared to other sugar moieties. It has been proposed that these dendrimers can be potential delivery vehicles for the treatment of glioblastoma and other types of cancers [124]. Furthermore, Zhang et al. (2020) utilized in situ growth approach

for the preparation of novel PAMAM dendrimers (0.5, 1.5, 2, and 2.5 generation) that are grafted on the persistent luminescence nanoparticles (PLNPs) surface. The aptamer AS1411 was coupled with the nanoparticle-dendrimer to specifically bind with the over-expressed nucleolin on the tumor cell membrane, thus increasing the intracellular accumulation of the nanoparticle. The PLNPs were spherical and 15.2 nm of average particle size. In addition, an anticancer drug named doxorubicin was also loaded in the dendrimer-nanoparticlebased delivery system via a pH-sensitive hydrazine, which was identified to be released in the targeted site of the intracellular acid environment. The new drug delivery system using dendrimers was found to have the capability of inducing apoptosis in HeLa tumor cells and inhibiting tumor growth [125]. The results of these studies suggest that dendrimers, particularly PAMAM dendrimers, have the potential to serve as effective drug delivery systems for treating specific diseases. Apart from drug delivery, dendrimers are also utilized as a potential gene delivery system. Ebrahimian et al. (2022) developed a novel lipo-polymeric PAMAM dendrimer-liposome, that are functionalized with transactivator of transcription (TAT) peptide and hyaluronic acid for targeted gene delivery system development. The study showed that the lipo-polymer possesses no significant toxicity with enhanced transfection efficiency in murine colon carcinoma cell line (C26), which will be beneficial for gene delivery applications [126]. Additionally, the incorporation or encapsulation of metals and metallocomplexes within the dendritic scaffold (metallodendrimers) holds the potential to yield novel metallodrugs or offer a new approach for the in situ delivery of metallodrugs. In this area, several works with in vitro and in vivo results were published by Rodrigues and coworkers, involving ruthenium compounds (an alternative metal for platinum-based anticancer resistant drugs) (Figure 6) [127,128], as well as platinum and platinum derivatives as anticancer drugs [128,129]. In any case, the research of the dendrimers is not exclusively restricted to the preparation of drug delivery systems. For instance, Rodrigues and coworkers developed new anionic poly (alkylideneamine) dendrimers until generation 3 with carboxylate and sulfonate terminal groups. These dendrimers have shown in vivo, to be very effective as microbicide against HIV-1 infection [130]. Thus, novel dendrimer structures were developed by the team, where their ability for targeted and controlled drug delivery is expected to be improved in the future.

**Figure 6.** In vivo studies in an MCF-7 xenograft mouse model of low-generation (0–2) nitrile poly(alkylidenamine)-based ruthenium dendrimers. Reproduced with permission from Maciel et al. [127] Copyright 2022 Royal Society of Chemistry.

### **8. Janus-Dendrimer Particles in Target-Specific Delivery Applications**

The enhanced targeted drug delivery ability of Janus structures and dendrimer nanoparticles has led to the emergence of novel Janus-dendrimer nanoparticles to improve their controlled delivery. Further, Janus-dendrimer possess exclusive properties and structures in combinations with different end-group types and distinct surfaces, which makes them a better candidate to form unique drug complexes and conjugates [131]. These Janusdendrimer particles were fabricated based on chemo-selective coupling, heterogenous double exponential growth, and mixed modular approach to be useful for stochastic and multiple drug conjugation-based combination therapy, solubility enhancement, antioxidant lyophilization, targeted delivery, and as fluorescent labels. In addition, the self-assembled Janus-dendrimer particles were identified to be beneficial for the vesicular delivery system, especially for pH-responsive delivery, targeted delivery, spatiotemporal delivery, and sitespecific drug delivery [132]. Pan et al. (2012) created a new Janus-dendrimer particle that was amphiphilic and had peripheral groups made up of acidic amino acids and naproxen molecules, designed for efficient drug delivery to bones. The second-generation dendrimers with >95% of binding rates towards hydroxyapatite in bones, 28-fold enhanced the solubility of naproxen for about 5.37 mg/mL of concentration, compared to the standalone drug. Moreover, the study also revealed that the Janus-dendrimers do not possess any significant cytotoxicity toward HEK293 cells [133]. Further, Iguarbe et al. (2019) created an effective liquid crystal Janus-dendrimer particle made up of mesogenic blocks based on two third-generation Percec-type dendrons with terminal dodecyloxy alkyl chains and one or two carbazole units serving as the electrically active component. The study reported that the carbazole dendrimers were prepared via electrodeposition to form semi-globular particles with electro-polymerizable units. Also, the resultant particles were able to retain the rigid or flexible characteristics of the linker, which eventually influences the size of the particle [134]. Furthermore, a link was established between hydrophobic paclitaxel and the Janus PEGylated peptide dendrimer by Li et al. (2017), through the use of an enzyme-sensitive glycylphenylalanylleucylglycine tetrapeptide as a connector, using an efficient click reaction as shown in Figure 7. The resultant Janus-dendrimer particle possesses the ability to encapsulate 21% of paclitaxel with an average hydrodynamic size of ~69 nm, a narrow polydispersity index of 0.23, the zeta potential of −16.9 mV and spherical morphology. The study showed that the Janus-dendrimer nanoparticle release paclitaxel via enzyme responsive feature and is identified to be highly cytotoxic towards 4T1 (murine breast) cancer cells without any toxicity against normal cells, such as 3T3 murine fibroblast and C2C12 murine myoblast cell lines [135].

Recently, Falanga et al. (2021) created a new type of Janus-like dendrimer that incorporates peptides derived from the glycoproteins (gH and gB) of Herpes Simplex Virus Type 1 (HSV1), aimed at inhibiting viral infection. This was achieved through the combination of copper-catalyzed bio-orthogonal 1,3-dipolar azide/alkyne cycloaddition and photoinitiated thiol-ene coupling, producing both monofunctional and bifunctional peptidodendrimer conjugates. The study revealed that the peptides released by the formulation possess enhanced antiviral activity by inhibiting the DNA replication of HSV1, compared to conventional antiviral drugs, such as foscarnet, acyclovir, and cifofovir [136]. Similarly, Najafi et al. (2020) prepared a novel poly (propyleneimine) (PPI) dendrimer of the fifth generation with a core of cystamine and a hydrophobic surface. Later, the structure scission approach was used to convert disulfide bonds to thiol group and hydrophilic PAMAM dendrons were formed with amine end groups. The study demonstrated that the Janus-like dendrimer, with an average hydrodynamic size of 4.2–28.2 nm, has the ability to enhance the solubility of hydrophobic drugs such as dexamethasone and tetracycline [137]. Likewise, Zhang et al. (2022) designed a hydrophobic multifunctional sequence-defined ionizable amphiphilic Janus-dendrimer region via dissimilar alkyl lengths. The research found that the Janus-dendrimer particles greatly enhanced the activity of the hydrophobic 3, 5-, 3, 4-, and 3,4, 5-substituted phenolic acids they encapsulated, by up to 90.2 times [138]. All these studies showed that the Janus-dendrimer nanoparticles possess enhanced drug delivery capacity, compared to standalone Janus particles and dendrimers.

**Figure 7.** Schematic representation of PEGylated Janus-dendrimer and their self-assembly for intravenous injection in murine models for enhanced stability and anticancer efficacy. Reproduced with permission from Li et al. Ref. [135]. Copyright 2017 ACS.

### **9. Limitations and Future Perspective**

Dendrimers, Janus particles, and Janus-dendrimer nanoparticles have been identified as potential candidates for improved drug delivery applications. However, there remain several limitations that must be overcome before they can be utilized in large-scale commercial applications. The synthesis of Janus particles is one of the major limitations, as it is a multi-step process that can be tedious and challenging to achieve sub-micrometer particle size [139]. The various synthesis approaches, including masking, phase separation, microfluidics, E-jetting, emulsion, and self-assembly, each have their own drawbacks which hinder their scalability. Additionally, general limitations of nanoparticles such as polydispersity, size-dependent toxicity, and the use of hazardous chemicals in synthesis are also applicable to Janus nanoparticles [140]. Dendrimers also face several limitations, particularly at generations higher than 3–4, including high cost of synthesis, a lack of understanding of their effects on biochemical pathways and toxicity, and difficulties in engineering multifunctional dendrimers, especially the tediousness in purification and the yields of the end product. The toxicity of dendrimers has been reported in various studies and can result from improper processing, leading to undesirable side effects, low tolerability, and inefficient drug delivery [141–143]. To address these limitations, the use of lower dendrimers generations with less toxic terminal groups and the use of computational software and machine learning approaches have been employed to optimize Janus, dendrimer, and Janus-dendrimer synthesis parameters. These computational approaches have the potential to reduce the number of experiments and the cost of production, while providing greater insight into the interaction of nanoparticles with biological environments. However, these computational approaches are still in the early stages of research, are highly dependent of the data availability, particularly with regards to the synthesis of nanoparticles and evaluation of their properties and interactions in biological environments [144,145]. In conclusion, the future of Janus and dendrimer nanoparticles for improved drug delivery will likely benefit from a combination of enhanced electron microscopes, synthesis techniques, computational approaches, and machine learning techniques. This will enable the

synthesis of novel and highly efficient nanoparticles with improved bioavailability for drug delivery applications.

### **10. Conclusions**

Recently, there has been an increasing interest in developing a novel drug delivery system that can enhance targeted and controlled drug delivery while reducing toxicity and increasing biocompatibility. Janus and dendrimer particles have been introduced as potential drug delivery systems due to their unique properties compared to conventional delivery systems. These particles have the ability to encapsulate and release drugs in a controlled manner, acts as a drug per se, which can improve the efficacy of drugs and reduce their side effects. However, there are limitations to the use of these materials in commercial pharmaceutics, including lack of scalability, high production cost, and the use of toxic chemicals in synthesis. The emergence of Janus-dendrimer particles holds promise for overcoming these limitations. The combination of Janus and dendrimer particles can result in enhanced drug delivery and improved biocompatibility. However, it is important to improve the stability and toxicity of these particles in the future. This can be achieved through simulation, modeling-based computational and machine-learning approaches, as well as by optimizing the synthesis process and reducing the use of toxic chemicals. With these advancements, Janus-dendrimer particles could become a key tool in advancing drug delivery technology and improving patient outcomes. Their utility extends to various applications within the healthcare field and beyond, opening up new possibilities for innovation.

**Author Contributions:** Writing—original draft preparation, J.J. and K.X.T.; writing—review and editing, J.R. and M.K.D.; supervision, J.R. and M.K.D. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Data sharing not applicable. No new data were created or analyzed in this study. Data sharing is not applicable to this article.

**Acknowledgments:** The authors (J.J. and J.R.) acknowledge FCT-Fundação para a Ciência e a Tecnologia (Base Fund UIDB/00674/2020 and Programmatic Fund UIDP/00674/2020, Portuguese Government Funds). All the authors thank their respective department for the support during preparation of this article.

**Conflicts of Interest:** The authors declare no conflict of interest.


Communication and Society

**51**

**Chapter 4**

**Abstract**

Scholarly Communication and the

This chapter focuses on the role that academic libraries play in the process of scholarly communication and presents a mixed-methods study to investigate (a) how faculty members perceive the involvement of academic librarians in scholarly communication and (b) how academic librarians perceive their own abilities to be involved in this process. The research population included faculty members from the faculties of humanities and social sciences in three Israeli academic institutions and academic librarians working in the libraries affiliated with these faculties. Interviews regarding the role of academic librarians in scholarly communication indicated wide gaps between faculty members and academic librarians and between individual members of each group, while questionnaires showed that a similar percentage of librarians and faculty members believe that academic librarians are potentially capable of being involved in this process. However, when asked whether the academic librarians should be involved in scholarly communication, only 36% of the librarians answered positively, as compared with 55% of the faculty members. These gaps highlight the need for change in academic libraries, as librarians should possess adequate technological skills, broad general knowledge, and an understanding of how to reorganize the

library work so as to accommodate collaborations with faculty members.

academic librarians, faculty members

**1. Introduction**

**1.1 Human communication**

vis-à-vis life in the modern world.

**Keywords:** scholarly communication, science communication, academic library,

Communication enables interpersonal transfer of messages and ideas and is a basic component of human interactions. Of all manifestations of human communication—e.g., facial expressions, body gestures, signs, or drawings—language appears to be the most complex, as it enables people to express complex ideas using a very wide range of words, subjects, and expressions, constructed into elaborate sentences. While researchers believe that language, or at least the ability to express thoughts by using words as means of communication, expression, and survival, is around 150–200 thousand of years old [1], it has evolved considerably with time, human biological and technological development, and changes in the human way of living, such that new words and expressions have been shaped to express ideas

Academic Library: Perceptions

and Recent Developments

*Liat Klain-Gabbay and Snunith Shoham*

## **Chapter 4**


**Abstract**

Diabetes mellitus is described by high blood glucose level resulting from deficiencies in insulin secretion, insulin action, or both. Type 1 diabetes is a condition in which pancreatic beta-cell get destructed and leads to absolute insulin deficiency. Lack of insulin causes hyperglycemia, polyuria, polydipsia, polyphagia, body mass loss, dehydration, electrolyte disturbance, and ketoacidosis. MNT necessitates an individualized tactic and effective nutrition self-management education, recommendation, and support. A key component of MNT is the provision of adequate calories for normal growth and development for children and adolescents with T1DM. The patient should monitor their saccharide intake either through saccharide counting or meal planning exchange lists for flexibility and variety in meals. Saccharide intake from whole grains, vegetables, fruits, legumes, and dairy products, with an emphasis on foods higher in fiber and lower in glycaemic load, should be advised over other sources, especially those containing sugars. Saccharide counting is helpful for people with diabetes in managing blood glucose level by tracking the grams of saccharide consumed at meals. All persons with T1DM need a substitute of insulin that mimics normal insulin action. An insulin-to-saccharide ratio can be established for an individual that will guide determinations on the amount of mealtime insulin to infuse.

**Keywords:** type 1 Diabetes, medical nutrition therapy, carbohydrates counting, glycemic index, glycemic load, insulin, insulin regimens, exchange lists, serving sizes