The experiments with the lip measurements were carried out without an exact repositioning scheme, meaning that the position of the spectroscopically recorded lip area of ca 2 mm diameter was randomly distributed across an area of roughly one cm2. Repositioning of the optical probe, for example, during an experiment using a rat animal model (see ref. [32]) led to a significant scatter in glucose prediction, so that the quality of our lip spectral data with regard to reproducibility and low-noise must be highly rated, especially when the second-day spectra are taken into account with the test subject showing more routine in lip measurements. Despite the temperature control of the lip contact area of the accessory, still, temperature gradients can be manifested, as evident from a principal component analysis (PCA) of the spectral population [36]. The dominating factors stem from the water spectrum and its temperature dependency, but also other features arising from methylene stretching overtones of the long-chain acyl groups found in the subcutaneous fatty tissue become visible.

In Figure 6, the time-dependent reference blood glucose concentration values are displayed together with the SBC predictions. The predicted and reference glucose concentration values had been day-wise mean-centered to adjust for a constant offset experienced here. The raw predictions were subtracted by 20 mg/dL to align with the fasting blood glucose level when starting the monitoring over the day. The offset makes sense to "high pass filter out" a noise component that was so far not included in the calibration **Σ.** As a possible explanation, we suppose that the pure water spectrum declared as "water displacement noise" and added to the estimate of **Σ**, was not quite appropriate, since there exist differences for the tissue water due to temperature and hydrogen-bonded molecules of various tissue compounds, which can be sensitively detected within the NIR spectrum. An idea of the complexity of water signatures within lip tissue can be obtained from a principal component analysis (PCA) of the lip spectra, which has been earlier illustrated [36]. At least the first five factors contributing to the spectral variance show features related to water and its dependencies on temperature gradients and differences in the hydrogen-bonded network.

**Figure 6.** Predictions of tissue glucose concentrations versus blood glucose values for the first day (**a**) and the second day of the modified oral glucose tolerance test (OGTT) (**b**); tissue concentrations were calculated with the SBC-calibration model using reflection lip spectra and glucose absorptivities scaled with photon penetration depth (results were offset corrected, see also text). Spectra and reference blood glucose concentration data are from previous publications [31,33]; calibration interval was from 8994–5477 cm−1 (115 data points) with noise covariance calculated from differences of lip spectra less than 8 min apart.

For the first day, during the starting phase, a slightly "running ahead" of the time profile of the tissue glucose is observed compared to the reference capillary concentration values from the fingertip, whereas for other periods, a significant time lag can be noticed. A similar lack of time correlation can be observed for the second day (this trace is also showing a reduced scatter when compared with the prediction data of the first day; note the difference in the *Y*-axis scales). Two days' worth of data from a single subject is not a large enough data set to allow quantitative statements about "typical" time behavior, but the following conclusions are clear. Relative to the time profile of the blood glucose concentration, the time profile in the skin can lead or lag. In several publications, it has been noticed that a glucose decrease in tissue can drop earlier than found for the vascular compartment, i.e., a glucose decrement in tissue precedes hypoglycemia [49]. On the other hand, also such a feature has been found for situations with a lead-in tissue for glucose increments (see experiment shown in [32]). The lack of time-correlation displayed by the examples in Figure 6 is concerning. The mechanisms causing this discrepancy will need to be studied in the future, and their effects quantified for given segments observed

for "typical" patients in typical environments. Especially for skin inserted CGM sensors, the time delay between blood and interstitial glucose profiles has been recently studied several times with two publications given here, one with three different sensors and a second paper dealing with an in silico study [50,51]. In particular, the in silico study is extremely interesting as glucose concentration differences of up to ±40 mg/dL between blood and interstitium were obtained, and time delays up to 25 min were realized. At any rate, even the simplest way of thinking about the glucose-in-the-skin as a time signal leads to a second-order differential equation, i.e., with two inputs, carbohydrate intake making the signal go up and injecting insulin, making it decrease. Which of these two effects wins the race to the measured skin and thus determines the slope (d/dt) of the signal at the time of measurement depends on what the subject did during the previous two hours. Given that people are a bit unpredictable even when performing routines, there cannot be an exactly fixed time-shift relationship to the glucose-in-the-blood. However, we can hope for more-or-less repeatable tendencies of patient behavior.

The SBC method also allows measuring the effect of the hardware noise floor only, i.e., by multiplying it into the b-vector for providing an estimate of the repeatability error, which was calculated to be 35.5 mg/dL RMS. For further comparison, with these data set at hand, several extensive studies have been carried out based on PLS calibrations, and the reader is referred to our publications [33,36]. Best standard errors of prediction could be achieved with variable selection, reaching a SEP = 36.6 mg/dL. Using an impulse invariant designed Butterworth filter of first-order with a time constants of 10 min, the time-dependent blood glucose profiles were shifted for allocating probably concentration reference values more similar to tissue estimates, but SEP improvements of only 2.5 mg/dL could be reached. Arnold and coworkers considered a time shift of 15 min between tissue and blood glucose profile for rat skin [32]. An offset similar as experienced within our SBC study could also be observed when using different daily data sets for calibration modeling; see results illustrated in [33]. Arnold and coworkers investigated the tissue variability and its impact on the PLS regression vectors. Differences in skin inhomogeneity led to vector changes with the effect of offsetting the prediction results.

Since there is no least-squares fitting to blood glucose values (or any other reference values) carried out in SBC calibration, the SBC predictions represent a direct measurement of the glucose concentration in the tissue. Unlike statistical calibrations, PLS, etc., which rely on the correlation between blood glucose reference values and skin spectra, and which are therefore influenced by the dynamic glucose transport processes between vascular and interstitial and intracellular compartments, SBC calibration is not influenced by these processes. It just measures the glucose in the skin. Therefore, it is rather useless to state SEP-values with systematic deviations as illustrated by in silico simulations, which yet considered two compartments only, i.e., vascular and interstitial space. The methodology applied by integral tissue spectroscopy will even show a larger complexity by taking the intracellular compartment additionally into account. To reach similar results with PLS calibrations, a large number of clamp experiments with steady-state conditions would need to be performed to furnish the analyst with the appropriate number of calibration samples and for allowing a comparison based on SEP or similar metrics used for sensor quality assessment such as MARD values (average of the absolute error between all CGM values and matched reference values). A comparison with other vibrational spectroscopy methods [16,17], recently published and mentioned in the beginning, is difficult. Results from the multiperson studies were given as MARD values of 12% for the mid-infrared spectral measurements with photothermal detection, and spectral outliers previously removed [16] or around 24% and larger for noninvasive Raman measurements [17]. For the SBC study, a MARD of 23% can be calculated with the omission of six extreme outlier data.

Since spectroscopy probes the integral tissue glucose whereas reference values used for validation rely on whole blood analysis, diffusion processes within the tissue, especially between the vascular and interstitial compartments leading to a variable temporal shift in both concentration time-series, will certainly need further investigations. It is not clear, unfortunately, whether it will be possible to predict capillary glucose concentration values uniquely from tissue measurements unless other experimental options for avoiding such complications are employed, e.g., the use of pulsatile spectroscopy (plethysmography).

#### **5. Conclusions**

The validation of calibration models for a noninvasive and transcutaneous blood glucose assay based on NIR spectrometry of skin illustrates a rather critical aspect. For the first time, the so-called "spectral Wiener filter" method, also known as a science-based method of calibration (SBC), was applied to noninvasive glucose measurement. The accuracy was tested on time-sequenced NIR-spectra collected over two OGTT days with better performance on the second day, which allowed conclusions about the behavior of the glucose concentration–time profiles in the blood and in the measured skin volume.

The accuracy achieved is far from that required for a viable noninvasive measurement. Still, the results are useful in a number of ways. First, they demonstrate that SBC calibration is possible also in the noninvasive case, i.e., that workload of calibration can be drastically diminished compared to today's routine practice with PLS usage. Second, the results indicate that also in the noninvasive case, glucose can be measured in a selective way, i.e., without using unspecific correlation effects like water displacement as a signal, which statistical calibrations have done in the past [35]. However, more work is needed here. The precision of the presented FT-NIR measurement is not good enough, i.e., the prediction scatters error is too large to allow reliable assessment of the magnitude of the prediction slope, or rather: deviation from the ideal value of unity slope. The latter is necessary for an exact, quantitative prove of selectivity of response [39]. The results shown in Figure 6 indicate a slope close to the desired value of one; for further discussion, see also ref. [33]. The accuracy of the scaling applied to transform the aqueous glucose absorptivity spectrum into the response spectrum of the noninvasive case is hard to estimate but is believed to be within several 10% of the true scaling, which is supported by the prediction of the blood glucose concentration range.

The robustness of calibration also must be improved in the future, i.e., additional variance contributions need to be added to the noise estimate **Σ** used in our SBC calibration (with the corresponding potential price in sensitivity) [35]. With in vivo spectroscopy, the variability from physiology, problems with repositioning of skin tissue, temperature gradients, blood flow effects, photon penetration depths, etc., are required to be further studied before calibration robustness for such application can generally be proven (e.g., avoiding systematic errors like the daily offsets experienced in our data set). The SBC prediction results also reveal that a different type of instrument will be more advantageous in the future because FT-NIR spectrometers are influenced by a relatively large hardware (detector) noise in the NIR-spectral range. In summary, the SBC calibration produced in the above way relies entirely on spectroscopy data and knowledge and does not use the laboratory reference values at all. In this regard, it is similar to the established classical calibration methods, but unlike these spectral fitting methods, the SBC result converges against, and therefore in praxis comes much closer to the optimal Wiener filter result.

The fundamental problem to be overcome in the future, however, seems to be the lack of time correlation between the glucose concentrations within the different tissue compartments, i.e., within the vascular, interstitial, and intracellular space. While the relationship of the temporal glucose profiles from the intravascular and the interstitial compartments was studied intensively by various needle-type sensors, the intracellular space also with phosphorylated glucose has so far been paid no attention apart from a few attempts. The effects causing a mismatch between the glucose concentration in blood and integral skin tissue need to be further evaluated. Though the optical spectroscopic methods have always caught much attention, and many different approaches with demanding hardware have been presented during the last 20 years, there seems to be no break-through hovering on the horizon using NIR-spectroscopy of skin tissues. On the other hand, the

SBC method can be considered a strong chemometric tool for regression and modeling tasks, and further work into noninvasive testing will be worthwhile.

**Author Contributions:** Conceptualization, methodology, validation, supervision: H.M.H. and R.M.; funding acquisition and project administration: H.M.H.; software development: R.M.; data analysis and visualization: S.D.; writing—original draft preparation, review, and editing: H.M.H., S.D. and R.M. All authors have read and agreed to the published version of the manuscript.

**Funding:** Former financial support by the German Research Foundation (DFG) is gratefully acknowledged (grant number HE 1685/1).

**Institutional Review Board Statement:** The study was conducted according to the guidelines of the Declaration of Helsinki, valid in 1991 when the experiments were carried out, and approved by the Review Board of the former Diabetes-Forschungsinstitut, Düsseldorf, Germany.

**Informed Consent Statement:** The test person had given his informed consent for inclusion before he participated in this study.

**Data Availability Statement:** The data presented in this study has been described in more detail in previous publications; see refs. [33–36]; the data presented were reanalyzed with novel, innovative chemometric tools.

**Acknowledgments:** We thank our former project partner T. Koschinsky for support and assistance in medical affairs.

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

#### **Abbreviations**

(AU) absorbance units; (DCCT) diabetes control and complications trial; (MARD) mean absolute relative difference; (NAS) net analyte signal; (NIR) near-infrared; (OGTT) oral glucose tolerance test; (PAT) process analytical technology; (PCA) principal component analysis; (PLS) partial least-squares; (PCR) principal component regression; (RMS) root mean square; (SBC) science-based calibration; (SEP) standard error of prediction, (SMBG) self-monitoring of blood glucose.

#### **References**

### *Article* **Using CNN and HHT to Predict Blood Pressure Level Based on Photoplethysmography and Its Derivatives**

**Xiaoxiao Sun 1,2, Liang Zhou 1,\*, Shendong Chang 3 and Zhaohui Liu 1**

**Abstract:** According to the WTO, there were 1.13 billion hypertension patients worldwide in 2015. The WTO encouraged people to check the blood pressure regularly because a large amount of patients do not have any symptoms. However, traditional cuff measurement results are not enough to represent the patient s blood pressure status over a period of time. Therefore, there is an urgent need for portable, easy to operate, continuous measurement, and low-cost blood pressure measuring devices. In this paper, we adopted the convolutional neural network (CNN), based on the Hilbert–Huang Transform (HHT) method, to predict blood pressure (BP) risk level using photoplethysmography (PPG). Considering that the PPG s first and second derivative signals are related to atherosclerosis and vascular elasticity, we created a dataset called PPG+; the images of PPG+ carry information on PPG and its derivatives. We built three classification experiments by collecting 582 data records (the length of each record is 10 s) from the Medical Information Mart for Intensive Care (MIMIC) database: NT (normotension) vs. HT (hypertension), NT vs. PHT (prehypertension), and (NT + PHT) vs. HT; the F1 scores of the PPG + experiments using AlexNet were 98.90%, 85.80%, and 93.54%, respectively. We found that, first, the dataset established by the HHT method performed well in the BP grade prediction experiment. Second, because the Hilbert spectra of the PPG are simple and periodic, AlexNet, which has only 8 layers, got better results. More layers instead increased the cost and difficulty of training.

**Keywords:** blood pressure; photoplethysmography; derivatives of PPG; convolutional neural network; ensemble empirical mode decomposition

#### **1. Introduction**

Patients with chronic hypertension will experience serious consequences if it is left untreated, including a range of cardiovascular diseases affecting the heart [1]. But most patients have no obvious symptoms in the early stages of the disease, so it is important to check BP level regularly.

The traditional method of BP measurement uses a cuff-link-type BP meter. The "white coat effect" refers to patients taking it in a medical setting with even less accurate BP than when they take it at home [2,3]. Therefore, a single measurement datum is not enough to reflect the human condition.  In view of the shortcomings of clinical invasive continuous BP measurement, which is difficult to perform and may cause serious harm to patients, noninvasive continuous BP measurement is of great significance.

With increases in age and changes in the physical condition of human beings, the elasticity of the blood vessel wall will decrease. When the resistance of blood flowing in blood vessels increases, blood pressure will increase accordingly. This is the pathogenesis of hypertension. Currently, there are three most commonly used noninvasive continuous

**Citation:** Sun, X.; Zhou, L.; Chang, S.; Liu, Z. Using CNN and HHT to Predict Blood Pressure Level Based on Photoplethysmography and Its Derivatives. *Biosensors* **2021**, *11*, 120. https://doi.org/10.3390/bios11040120

Received: 19 February 2021 Accepted: 9 April 2021 Published: 13 April 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 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 (https:// creativecommons.org/licenses/by/ 4.0/).

55

BP detection methods. The first is the pulse transit time (PTT) theory method, which refers to the time difference of the diffusion of pulse waves between two arteries in a cardiac cycle. When BP in the blood vessels increases dramatically, the vascular tension and speed of arterial pressure waves will increase, leading to shortened PTT [5]. However, obtaining the time difference requires both electrocardiogram (ECG) and pulse wave signals. It is also difficult to acquire the ECG. The second detection method is based on the morphological theory of PPG [4,6,7]. The most common way to obtain the PPG signal is to use a photoelectric sensor to detect changes in light transmitted or reflected by human skin vessels [8]. PPG represents the change of human blood volume and characterizes the systolic and diastolic processes of the heart, which are linked to BP. PPG s first and second derivative signals are related to atherosclerosis and vascular elasticity, which are factors that influence BP. Luo Zhichang et al. [9] found that the tidal wave of the pulse wave signal of high blood pressure will gradually approach the main wave, finally merge with it, and even exceed the main wave. Therefore, the main wave of the PPG signal with hypertension looks more rounded and curved than that of normotension. Thus, PPG is closely related to BP. Therefore, PPG signal is increasingly applied to predict BP, blood oxygen, respiration rate, and other data [10–12]. The third method combines the characteristic points of the ECG and PPG signals to predict BP [13–15]. The reason for the rise of this method is that, although PPG signals feature points that contain information related to SBP (systolic blood pressure), it is not easy to determine the relationship between PPG and DBP (diastolic blood pressure). Therefore, using PPG alone is bound to lead to inaccurate BP predictions. Accuracy is improved by adding the ECG signal [16]. However, obtaining the ECG signal remains a challenge in research because of current technology limitations.

Among the many studies on PPG signals, research on the derivatives of PPG has attracted our attention. Figure 1 shows PPG and its first and second derivatives. Qawqzeh et al. [17] analyzed the relationship between the first and second derivatives of PPG and atherosclerosis. The second derivative of PPG (SDPTG) was found to be closely related to atherosclerosis and could be used as an assistant technology to detect the disease. There are many factors causing atherosclerosis in the human body, among which hypertension is the most common one. Hypertension and arteriosclerosis cause and affect each other and exist together. Based on this, Mengyang Liu et al. [18] used PPG and its second derivatives to predict noninvasive BP. They retrained the experiment by adding 14 SDPTG features based on the 21 time-scale PPG features, and the experimental results showed that SDPTG can improve the accuracy of BP prediction. However, in real life, the morphological characteristics of PPG and SDPTG vary from person to person, and there are certain difficulties in the calibration and measurement of features, which bring great difficulty to the research. Considering the difficulty mentioned above, many scholars have innovatively used CNN to indirectly identify and extract feature points without manual calibration [19,20], which greatly reduced the time-cost. The self-adaptability of the network structure to extract feature points improves the universality of the experiment and makes the results more convincing. In addition, Liang et al. [21] found that the method using CNN has a higher accuracy than the method using PPG feature point fitting.

**Figure 1.** PPG (photoplethysmography) and its first derivative (PPG ) and second derivative (PPG"). The data comes from the MIMIC database.

The purpose of our paper was to use PPG and its first and second derivative signal to predict BP level using a deep learning method. First, we segmented the PPG, extracted the baseline, and regularized the signal. Then the Hilbert–Huang Transform method was used to process the segmented signal and the generated image, and the corresponding BP value of the signal was taken as the label. Finally, the CNN was used to train the dataset. We looked at three questions; the first is to determine whether the training results of the dataset established by the HHT method are good enough on the network. The second is to explore whether more layers in the network are better for our datasets. The third is to know whether the first and second derivatives of PPG carry BP-related information.

#### **2. Materials and Methods**

#### *2.1. Dataset Source*

Both the PPG and the ABP (arterial blood pressure) data used in this paper come from the MIMIC database. Each signal has 10 s length and a sampling frequency of 125 Hz. In order to improve the generalization performance of the training frame and the generality of the experiment, in addition to the continuous and stable signal, the continuous unstable signal, and the noisy signal were retained. We divided the two signals into 2 parts for 5 s each. After signal processing—including using 0.4–8 Hz Butterworth filter as filtering, normalization, and baseline drift removal—the maximum values of systolic and diastolic blood pressure were extracted from each segment of the ABP signal, and the BP level corresponding to this segment of signal was determined by the 7th Report of the Joint National Committee on Hypertension Prevention, Detection, Evaluation and Treatment (JNC7). By analyzing the values of systolic and diastolic blood pressure (Table 1), JNC7 classifies blood pressure into three categories: normotension (NT), prehypertension (PHT), and hypertension (HT). The BP level shown by the ABP serves as the label for the corresponding segment of the PPG signal.

**Table 1.** Blood pressure classification according to JNC7.

#### *2.2. Signal Processing*

PPG is a time-domain, nonlinear, and unstable human physiological signal. Therefore, processing the signal can not only retain as much information as possible contained in the original signal but can also provide images that meet the requirements for the deep learning model required by the experiment, which is the main problem we studied in this section.

Slapniˇcar et al. [22] converted PPG and its first and second derivative signals into spectrum diagrams and input three types of spectrum diagrams into the ResNet network for training. This method required a good PPG signal, otherwise the derivative signals would become seriously deformed. Liang et al. [21] used the features of PPG to convert each segment of 5 s signal into a scalogram by using continuous wavelet transform (CWT). CWT is based on the basis function pair, which lacks adaptability and is not easily used to comprehensively describe the complex physiological characteristics of PPG, which is nonlinear and nonstationary. To make up for the shortcomings of previous studies, we decided to use the Hilbert–Huang Transform (HHT), which has the following advantages:

• One-dimensional signals are converted into two-dimensional signals to facilitate deep learning.

#### *2.3. HHT Based on Ensemble Empirical Mode Decomposition*

The Hilbert–Huang Transform was proposed by N. E. Huang [23], who added the empirical mode decomposition (EMD) method on the basis of the Hilbert Transform (HT). EMD (the algorithm flow chart shown in Figure 2) can decompose nonstationary complex signals into intrinsic mode functions (IMFs). Huang et al. believed that any signal is composed of several IMFs; by filtering out the IMF components represented by the highfrequency noise signal and motion artifacts, EMD can realize the smoothing processing of nonstationary signals. By this method, the problem of morphological malformation of the PPG s derivative signals caused by motion artifacts of PPG could be alleviated [24,25]. The IMF components of different frequencies of the decomposed PPG signal represent different physiological meanings. Previous studies have confirmed that different frequency ranges of decomposed IMFs represent different physiological activities. Mitali et al. [26] obtained surrogate respiratory signals at 0.2 Hz to 0.33 Hz. Chuang et al. [27] obtained the related information regarding heart rhythm at 0.4 Hz to 0.9 Hz. IMFs contain the local characteristics of the original signal at different time scales, so they can retain as much of the original information and characteristics of a signal as possible. Since the basis function is obtained by the EMD decomposition of the data itself, compared with the short-time Fourier transform and wavelet decomposition, the EMD method is adaptable, and the signal becomes more direct and intuitive after the Hilbert transform (Figure 3). Hilbert transformation can be performed on the decomposed IMFs to obtain spectra as inputs to the network.

**Figure 2.** EMD (empirical mode decomposition) processing algorithm. IMF (intrinsic mode function) conditions: (1) In the entire time range, the number of local extreme points and zero points of the function must be the same or differ by one at most. (2) At any point in time, the average value of the envelope of the local maximum of the function (upper envelope) and the envelope of the local minimum (lower envelope) must be zero.

**Figure 3.** Process of processing PPG signal and its derivatives by the HHT (Hilbert–Huang transform) method.

However, when processing noise signals and intermittent signals, the EMD method will lead to the phenomenon of mode mixing, which seriously affects the accuracy of signal decomposition. To remedy this disadvantage, Huang et al. [28] added white Gaussian noise into the whole time-frequency space for several amounts of time, and several mean values of IMF components were obtained as the final result, which was called EEMD (ensemble empirical mode decomposition). The mode aliasing phenomenon was effectively suppressed by the EEMD method. Besides, this method could be used to remove the motion artifact of PPG signals effectively. To improve the generalization performance of the model, in addition to the serious baseline drift of some PPG signals, we selected some signals with high noise. Therefore, in this paper, we adopt the EEMD method, because this method is better than the EMD method to deal with noisy signals.

The signal processed by the EEMD method is still a one-dimensional vector. Compared with this data format, the features of the signal shown by the two-dimensional image are more prominent. Combining the advantages of convolutional neural networks in the field of image recognition and its own characteristics, images with more prominent feature points require fewer layers, fewer calculations, and less complexity. Furthermore, the spectra of the PPG signal after HHT are related to the time-instantaneous frequency–energy distribution map. Observing the spectra of normotension and hypertension samples (Figure 4b), we found that the instantaneous frequency of the energy distribution in the spectra of the latter is higher than that of the former. Such significant information cannot be obtained using a one-dimensional vector to represent the PPG signal. Therefore, we used a two-dimensional signal as the input of the convolutional neural network.

We built the PPG+ dataset [29]; the PPG s Hilbert spectrogram is the red channel of the RGB image. The spectrograms of the PPG s first and second derivatives are the other two channels (Figure 3) to improve the information dimension and enhance the image s extractable features. These three channels combine to form an RGB image with resolution of 1247 × 770 (Figure 4c); the image length is 5 s (0–5 s), and the width is 13 Hz (0–13 Hz). The sampling quality of the images is 24 bytes, which is 8 bits for each channel and 256 levels. As the input to the network, HT spectra require preprocessing such as random aspect ratio cropping, random flipping, and center cropping before entering the network. This approach is important to enhance the data. Then, we normalized the processed image, and the image size became 3 × 224 × 224. Furthermore, the control experiment's inputs were only the PPG signals spectrograms.

**Figure 4.** HHT spectrograms of three patients in different time periods. (**a**) PPG signals. (**b**) HHT spectra of PPG signals. (**c**) RGB images of PPG and its first and second derivative (PPG+) spectrogram combination. No. 408, No. 230 and No. 224 are the numbers of the patients.

#### *2.4. Model Fine-Tuning*

Transfer learning is a branch of machine learning. The aim is to deal with a new problem by transferring it to a problem that has been resolved. Transfer learning can greatly reduce the experimental costs and training time of deep learning models and also can be applied to the problem of small datasets. Many studies have shown that transfer learning can improve the generalization ability of models [30].

Fine-tuning is a widespread practice in transfer learning, and it is suitable for small datasets. One common practice is to remove the last layer of the pretrained network and replace it with a new softmax layer, which relates to a specific problem. Considering the characteristics of the model s inputs, we used the AlexNet [31], ResNet18 [32], ResNet34 and GoogLeNet to train the data. AlexNet has five layers of convolutional layers and three layers of fully connected layers. The ReLU, as the activation function, solves the gradient dispersion problem. ResNet adds a residual block through a short-circuit mechanism between every two layers of the ordinary network, transforms the difficult identity mapping problem into a residual problem, solves network performance degradation with increasing depth, and effectively alleviates the gradient dispersion or gradient explosion phenomenon of deep neural networks. GoogLeNet has 22 layers. It connects multiple inception blocks in series with other layers to improve the expressive ability of the network. Compared with other networks, GoogLeNet has fewer training parameters and faster convergence speed. The input size of GoogLeNet is 3\*299\*299, which is different than the other three networks.

In our work, the fine-tuning method removed the last fully connected layer when training with AlexNet, added Dropout to ignore some neurons to prevent the model from overfitting randomly, and finally added a fully connected layer with two outputs. When it came to ResNet18 and ResNet34, the specific method was to reinitialize the last fully connected layer and a linear layer with 512 input features and two output features. The parameters did not need to be trained from scratch but only fine-tuned based on the

original model parameters, which dramatically improved the training efficiency and saved on training costs. Figure 5 shows the preprocessing of the signal and the classification experiment.

**Figure 5.** PPG signals processing procedure and the classification experiment (AlexNet).

#### *2.5. Classification Experiment*

We built three classification experiments by collecting 582 data records from the MIMIC database: HT (hypertension) vs. NT (normotension), HT vs. PHT (prehypertension), and (HT + PHT) vs. NT. Two datasets, PPG and PPG+, were trained on the following networks with different layers: AlexNet, ResNet18, GoogLeNet, and ResNet34. Three two-classification experiments were performed on each dataset for every network. We designed the experiment for three purposes: First, to explore whether using our dataset to predict blood pressure levels could get better results. Second, to verify whether the PPG+ dataset containing the information of the PPG itself and its first and second derivatives was better than the PPG dataset for blood pressure prediction. Third, to consider whether the higher the number of layers, the better the prediction results would be.

The programming language used for the experiment was Python; the library used to call the model structure was PyTorch, and the model was trained on Anaconda. Code was run using a desktop with an Intel i5-8500 as the CPU, 8GB RAM, and AMD Raden R5 430 as the graphic card. After testing and optimizing the model many times, we chose Adam as the optimizer of the model, and the learning rate parameter of the optimizer was set to 0.001. Since our dataset was relatively small, we divided all data into training sets and test sets in to the ratio 7:3. To improve the generalization performance of the model and get more accurate results, we applied k-fold cross-validation to train the dataset and averaged the results. Taking into account the limitations of computer performance and the time-cost required for cross-validation, the value of k here was 5. We performed 25 epochs for each fold. The fine-tuned AlexNet used a total of 57.01M parameters, and the amount of calculation (floating point operations) was 0.71 GMAC (Memory Access Cost). After testing, the model worked best when the dropout value was set to 0.6 (for AlexNet).

#### **3. Results**

In this paper, we randomly selected 582 data records of ABP and PPG from the MIMIC database; each record was 10 s in length. We divided the 2 signals into 2 segments of 5 s each. Then the signal was filtered with a 0.4–8 Hz Butterworth filter and normalized. The EEMD method was used to remove the signal s motion artifacts and baseline drift. According to the blood pressure classification standard from JNC7, the ABP of each segment was analyzed and then classified as the label of the corresponding PPG signal. Then we performed Hilbert processing on the PPG and made two datasets of PPG and PPG+. The images of the PPG+ dataset contain the first and second derivatives of PPG in addition to the signal itself. Finally, we put these two datasets into four different layers of network models for training. The results (Table 2) are represented by the F1 score, TPR (true positive rate), and TNR (true negative rate).

**Table 2.** Classification performance of the proposed deep learning method. TPR stands for true positive rate and refers to the sensitivity of the model. TNR is short for true negative rate and refers to the specificity of the model.

We reached the following conclusions from the table:

From the results, the F1 score of AlexNet for our dataset was high: 98.90%. In the NT vs. HT experiment of AlexNet by using the PPG+ dataset as an example, TPR refers to the sensitivity of the model, which means that the number of normal blood pressure samples predicted by the model accounting for 99.27% of the actual normal blood pressure samples. TNR refers to the specificity of the model, which presents the sample size of hypertension predicted by the model to be 98.31% of the actual sample size of hypertension.

The ROC (receiver operating characteristic) curve (Figure 6) characterizes the generalization ability of the learner from the perspective of threshold selection. The closer it is to the upper left corner, the lower the classification error rate of the model, the better the threshold at this time, and the fewer false positives and false negatives there are. We found that the ROC curve of the experiment of NT vs. HT is closest to the upper left corner, which means our network performed best on it.

**Figure 6.** The ROC (receiver operating characteristic) curve of the three classification trials of AlexNet.

Comparing the classification accuracy of AlexNet on the training set and the test set (Figure 7), we found the epoch\_accuracy curve of the training set and the test set were very close, which means that the model performed well on our dataset, and there was neither overfitting nor underfitting. The accuracy curves of NT vs. HT and (NT + PHT) vs. HT were close to stable, indicating that the two have converged and the number of training epochs was sufficient. The accuracy of NT vs. PHT on the test set fluctuates greatly, and it has not converged yet, indicating that 25 epochs is not enough for this experiment. However, through the performance on the training set, we can basically infer the future trend of the accuracy curve of the test set. Therefore, upgrading hardware equipment and increasing the number of epochs in the experiment to improve the training effect is part of our future work.

**Figure 7.** *Cont*.

**Figure 7.** The diagram of epoch and accuracy (referred to as acc in the figure) of the NT vs. HT (**a**), NT + PHT vs. HT (**b**) and NT vs. PHT (**c**) in AlexNet.

#### **4. Discussion**

We used a convolutional neural network to predict blood pressure levels using PPG and its first and second derivative signals. The HHT method was applied to remove the baseline and motion artifact of PPG signals, and then we converted the one-dimensional signal into spectra as the input of the network. The blood pressure level represented by ABP was used as the label of each spectrum. According to the information contained in spectra, we established two datasets. One was called PPG+, which contained PPG itself and its first and second derivative signals. The other one was called PPG, which only contained the information in the PPG signal itself. Three binary classification experiments for each dataset were trained on four network models with different layers, AlexNet, ResNet18, GoogLeNet, and ResNet34. This study found that, although AlexNet had a small number of layers, the training results were still the best on both datasets. The fine-tuned AlexNet performed well in the NT vs. HT experiment, with an F1 score of 98.90%. The result of experiments confirmed that the first and second derivatives of PPG could improve training accuracy.

We noticed that the model performed best in NT vs. HT and performed poorly in NT vs. PHT. ROC showed that NT vs. HT was closest to the upper left corner; the distributions of these two types of data were far apart and easier to separate. The model performed poorly in the HT vs. PHT experiment because the two types of data distribution were much closer. In comparison, we also found that the PPG+ dataset had better performance than the PPG dataset in the BP classification model, especially in the NT vs. HT experiment. This confirmed that the first and second derivatives of the PPG signals carry information about BP.

The first derivative of the PPG signal represented the blood flow velocity in the aorta, and the second derivative signal represented the change in blood flow velocity (Figure 4c), which is decided by the blood viscosity and elasticity of the blood vessel wall. The hypertensive patients blood pressure is high, so when the aortic valve opens, blood flows into the aorta quicker. If the blood vessel elasticity is lacking, the PPG signal s descending branch will be steeper than in ordinary people. The second derivative of the PPG signal happens to reflect this. This is the reason that adding PPG derivative information to the dataset can improve the accuracy of BP prediction.

In order to improve on the weak points of previous studies (Table 3), this paper adopted the EEMD method to process signals. The resulting HHT spectra contained the physical meaning of a particular PPG signal. The PPG signal represents the change of blood volume in human blood vessels, and the derivatives of PPG signal contain information related to blood flow. Therefore, we added the first and second derivative signals of PPG signals to the model to train on simultaneously.

**Table 3.** Comparison with well-established related work in terms of data source, feature, signal processing, and method.

The purpose of converting a one-dimensional signal into a spectrum was not only to satisfy the input format of the network model but also to make the information contained in the PPG signal more prominent. The experimental results showed that the smaller the number of layers, the better the training effect on our dataset. This result proved our approach: the feature points become more obvious after the PPG one-dimensional signal is converted into spectra, and better results can be learned without too many layers of convolutional neural networks. This can reduce the cost of model training. Compared with image recognition and classification problems such as cats and dogs, the input images in this paper had fewer feature points and lower complexity. This might be one of the reasons that the accuracy decreased or remained unchanged with more network layers in the experimental results.

Since the dataset mentioned in the paper [21,22] was not available, we used GoogLeNet to train the dataset for comparison. The F1 score of NT vs. HT in the PPG+ dataset was 89.24%. Although the result was poor compared with previous research, this result could not be evaluated arbitrarily because of the different datasets. What was certain, however, was that this result again confirmed our point that our dataset performed better on a network with fewer layers. AlexNet with only eight layers performed better on both datasets than some other networks with more layers. On the contrary, GoogLeNet, which has many layers and many complex inception blocks, did not perform well on our datasets.

#### **5. Conclusions**

In this paper, we used the HHT method to establish a new dataset. The fine-tuned AlexNet performed well on our dataset. The F1 score of the NT vs. HT binary classification experiment can reach 98.90%. The signals processed by HHT have specific physical meanings and obvious feature points, which were conducive to the learning of neural networks. By comparing the performance of blood pressure classification experiments in different network models, our study proved that the derivatives of PPG carry important information on blood pressure, which means that PPG and its derivatives can be used to replace the combination of ECG and PPG for blood pressure prediction. We also found that a network structure with fewer layers had a better performance on our dataset. This can reduce the amount of calculation and the time-cost of network training. We combined the EEMD method with deep learning, providing new ideas for modern medical health testing while providing a noninvasive, fast, and low-cost BP level assessment method for families and low- and middle-income countries. However, this technology still has lots of room for improvement. Our next target will focus on how to improve the classification accuracy and how to predict BP values through deep learning and will explore more information related to physiological activities from PPG and its derivative signals.

**Author Contributions:** X.S. did validation, formal analysis, project investigations, data curation, writing–original draft preparation, graphic visualization and code writing. X.S. and S.C. reviewed and edited the manuscript together. X.S., S.C. and L.Z. collected resources. Z.L. and L.Z. are the project administrators. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the National Natural Science Foundation of China, grant number 61805275.

**Institutional Review Board Statement:** Ethical review and approval were waived for this study, due to all data are collected from open-downloadable datasets.

**Informed Consent Statement:** Patient consent was waived due to all data are collected from opendownloadable datasets which written informed consent should be already obtained by sharing institution and all participant agreed to disclose their data to research.

**Data Availability Statement:** The signal data in the MIMIC database were downloaded from the following website: https://archive.physionet.org/cgi-bin/atm/ATM. The dataset we created is available at the following link: http://figshare.com/articles/figure/Blood\_pressure\_classification\_ experiment/13498422/1.

**Conflicts of Interest:** The authors declare no competing financial or nonfinancial interest.

#### **References**

### *Article* **Novel Micro-Nano Optoelectronic Biosensor for Label-Free Real-Time Biofilm Monitoring**

**Giuseppe Brunetti 1, Donato Conteduca 1,2, Mario Nicola Armenise 1 and Caterina Ciminelli 1,\***

**Abstract:** According to the World Health Organization forecasts, AntiMicrobial Resistance (*AMR*) is expected to become one of the leading causes of death worldwide in the following decades. The rising danger of *AMR* is caused by the overuse of antibiotics, which are becoming ineffective against many pathogens, particularly in the presence of bacterial biofilms. In this context, non-destructive label-free techniques for the real-time study of the biofilm generation and maturation, together with the analysis of the efficiency of antibiotics, are in high demand. Here, we propose the design of a novel optoelectronic device based on a dual array of interdigitated micro- and nanoelectrodes in parallel, aiming at monitoring the bacterial biofilm evolution by using optical and electrical measurements. The optical response given by the nanostructure, based on the Guided Mode Resonance effect with a *Q*-factor of about 400 and normalized resonance amplitude of about 0.8, allows high spatial resolution for the analysis of the interaction between planktonic bacteria distributed in small colonies and their role in the biofilm generation, calculating a resonance wavelength shift variation of 0.9 nm in the presence of bacteria on the surface, while the electrical response with both micro- and nanoelectrodes is necessary for the study of the metabolic state of the bacteria to reveal the efficacy of antibiotics for the destruction of the biofilm, measuring a current change of 330 nA when a 15 μm thick biofilm is destroyed with respect to the absence of biofilm.

**Keywords:** bacteria biofilm; optoelectronic device; antimicrobial resistance; biosensing

#### **1. Introduction**

Bacterial infections represent one of the leading causes of death in developing nations [1]. The infections are caused by food poisoning, which is often related to water contamination or improper food preparation [2]. Furthermore, the large overuse and/or misuse of antibiotics is causing a rapid growth in AntiMicrobial Resistance (*AMR*) worldwide [3]. *AMR* is developed when the bacteria adapt to and resist antibiotics treatments, which become ineffective to counteract a bacterial infection that can grow and spread in a large community through direct contact, food, or the environment [4,5]. As a result of the lack of powerful antibiotics, many bacterial infections, such as pneumonia, tuberculosis, and gonorrhoea, are becoming more difficult to eradicate with a consequent higher mortality rate [6]. According to [7], the cost of *AMR* on public health is up to 100 trillion USD, and *AMR* is expected to become the leading cause of death worldwide, with over 10 million annually predicted by 2050. These consequences highlight that *AMR* is a widespread social problem that cannot be underestimated or neglected anymore due to the large and rising number of people potentially affected.

Many bacterial infections are caused by the non-eradication of bacterial biofilm, which can be several times more resistant to antibiotics compared to planktonic bacteria [8,9]. This behavior is strictly correlated to the intrinsic nature of the biofilm, which consists

**Citation:** Brunetti, G.; Conteduca, D.; Armenise, M.N.; Ciminelli, C. Novel Micro-Nano Optoelectronic Biosensor for Label-Free Real-Time Biofilm Monitoring. *Biosensors* **2021**, *11*, 361. https://doi.org/10.3390/bios11100361

Received: 26 August 2021 Accepted: 27 September 2021 Published: 29 September 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 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 (https:// creativecommons.org/licenses/by/ 4.0/).

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of densely packed microbial cells that can grow and surround themselves with a selfproduced Extracellular Matrix (*ECM*). The *ECM* is composed of proteins, polysaccharides, and nucleic acids that protect the bacterial biofilm from the environment, so making it very resistant to external agents, such as antibiotics [10]. Moreover, a biofilm may include different bacteria, and this makes the dissolution of biofilms more challenging [11]. In fact, it has been demonstrated that even if a biofilm is treated by an antibiotic that is efficient for a specific planktonic bacterium or small communities of bacteria with a concentration much higher than Minimum Biofilm Inhibitory Concentration (*MIC*), which represents the lowest concentration of drug to prevent the bacteria growth, the biofilm structure can be completely unaltered, showing a continuous growth process also after the treatment [12].

#### **2. Techniques for the Bacteria Detection and Analysis**

To date, the most widely used diagnostics to detect the presence of bacteria and analyze their evolution under several antibiotics' treatment is the plate-count method, which is based on the growth of bacteria on an agar plate [13,14]. However, this technique is time consuming (24–72 h), because it requires many cell-division cycles as well as expert users for the sample preparation and final analysis. The large time delay is the most significant bottleneck of such a technique because several infections, such as sepsis, require an immediate measure, also to avoid the formation of a biofilm [15]. The antibiotics are commonly administered in the clinicians' experience with a not-negligible failure rate, so possibly leading to an outbreak of the resistance rather than by carrying out an accurate diagnosis. Thus, novel diagnostic techniques that can rapidly detect and identify bacteria and confirm the presence of a biofilm, ideally within 30 min, are needed. Furthermore, these techniques should also enable study of the efficiency of antibiotics with a real-time analysis during the treatment in order to define the most powerful antibiotic, the best concentration, and the administration time for each infection [16].

During the last few years, several approaches have been investigated, mainly with the use of optical techniques, such as Raman Spectroscopy or fluorescence, due to their high resolution and real-time detection of individual bacteria [17–19]. However, these methods are not label-free, becoming inefficient in the presence of bacteria mutations, and allow investigating only a small area. Integrated optical devices have also been used for single bacteria analysis with label-free techniques [20]. In particular, resonant cavities are able to trap and identify single bacteria through the changes of the resonance response [21,22]. Emerging studies with a label-free optical-based approach have demonstrated real-time monitoring of cell attachment and the development of bacteria on the sensor surface [23]. Optical devices have been used to investigate the antimicrobial susceptibility with in vitro studies by adopting antibiotics with concentrations compliant with standard health protocols [23]. However, the simultaneous detection of several bacteria in the whole biofilm volume is still challenging because of the mismatch between the biofilm thickness and evanescent field penetration depth. In fact, the evanescent field of resonant cavities typically extends for few hundreds of nanometers, thus making impossible the detection of multiple bacteria organized in a three-dimensional configuration as a biofilm that can reach a thickness of several microns in the presence of macro-colonies in the maturation phase.

The mechanical trapping of bacteria has also been obtained by using microfluidic devices [24,25], allowing the bacteria localization in specific areas to accurately detect and analyze them with an atomic force microscope (*AFM*) [26]. The *AFM* technique guarantees a very high resolution, also providing relevant information about the metabolic state of bacteria and if they are live/dead, for example by observing a different motility with a change of amplitude and noise in the electrical signal [26]. However, the mechanical trapping has a trapping time not long enough to investigate the metabolic activity of bacteria and their interactions in large communities, so making also difficult their differentiation in the biofilm.

The aforementioned critical issues have been partly mitigated by Electrochemical Impedance Spectroscopy (*EIS*) [27–31]. In particular, device configurations based on interdigitated microelectrodes have been exploited for the analysis of the metabolic activity of bacteria and their differentiation [27]. This approach is characterized by a great penetration of the electric field in the biofilm, in contrast to the integrated optical devices where the distribution of the evanescent field of the optical resonant mode within the biofilm is limited [32], as demonstrated in the next sections. Therefore, the electrical behavior guarantees the capability of studying the biofilm evolution and the action of the antibiotics.

However, the lowest detection limit of *EIS* is around 10 CFU/mL [31], which means no high resolution down to single cells. Moreover, the detection time is longer compared to other trapping approaches, such as optical and mechanical ones, because, in case of low initial concentration, the growth of the cells is required before achieving a detectable change of impedance, which usually takes a few hours [33].

The resolution can be improved by different configurations of electrochemical biosensors, such as interdigitated nanoelectrodes, which compared to the bulk configuration of single electrodes allow the enhancement of the electric field in the device and then, a stronger interaction between the field and the bacteria [34]. However, if the resolution improvement is achieved at the expense of a shorter penetration length of the electric field, the analysis of the bacteria metabolism in a biofilm would be impossible.

From this brief overview on the techniques for the detection and analysis of bacteria, it is clear that a single interrogation technique is not sufficient for a full assessment of the antibiotic susceptibility on planktonic bacteria and, in particular, on bacterial biofilms. Multiple approaches should be used in parallel, leading to a multiparameter approach [35,36].

Here, we propose the design of a novel optoelectronic device based on an on-chip dual array of interdigitated micro- and nanoelectrodes that combines together optical and electrical techniques to monitor the growth of a biofilm and to analyze the effect of antibiotics on the bacteria. The optical approach allows the detection of few bacteria with a high spatial resolution to understand their interaction and which biological events are involved during the initial phase of the biofilm formation. The electrical approach allows the simultaneous study of the evolutionary phases of the bacterial biofilm and, by analyzing the impedance changes, the evaluation of the biofilm growth and maturation, and the efficacy of antibiotics for the disruption of the biofilm, which are useful in AMR studies.

#### **3. Dual Array of Interdigitated Electrodes: Architecture and Operation**

The dual array is formed by an Interdigitated Micro Electrodes (*IMEs*) section and an Interdigitated Nano Electrodes (*INEs*) section, which are both realized in Silicon-On-Sapphire (*SOS*) technology and fed by an *AC* voltage (see Figure 1a). In order to perform both optical and electrical measurements to study the properties of planktonic bacteria or a bacterial biofilm in the whole sensor area, the two sections are arranged adjacent, spaced from each other only by a few microns.

P-doping has been assumed for silicon, with an exponential decay of the electrical conductivity from the surface in depth with a drop more than three orders of magnitude in few tens of nanometers, as described in Section 4.1, reaching very low values of resistivity at the surface, and also strongly reducing the optical losses correlated to the dopant concentration [37]. The *INEs* section can be assumed as a top-illuminated subwavelength grating, whose cross-section is sketched in Figure 1, that supports the Guided Mode Resonance (*GMR)* effect [38]. It is correlated to the quasi-guided modes or leaky modes of the structure, as shown by the grating in Figure 1a. The grating acts as a waveguiding layer in the *x-y* plane, where the input light excites quasi-guided or leaky modes that coherently scatter at each interface of the grating. Furthermore, leaky modes scatter power downwards, along the vertical direction perpendicular to the grating (*z*-axis).

By properly engineering the grating features, such as the period, the refractive indices, and the angle of incidence, the interference between the transmitted light and the downward wave scattered by the leaky mode could generate reflected light along the negative direction of the *z*-axis with maximum amplitude at resonance [39,40].

**Figure 1.** (**a**) Configuration of the dual array of interdigitated micro- and nanoelectrodes in SOS technology for the electrical detection of bacteria (in green) growth and metabolism. The section with nanoelectrodes represents an optical sub-wavelength grating resonating at a specific wavelength when top illuminated, so enabling a simultaneous optical and electrical detection of bacteria deposition; (**b**) Interdigitated Micro Electrodes (*IMEs*), where *W* is the width of the silicon layer (*W* >> *w*), *G* is the gap between electrodes (*G* >> *g*); and Λ*m* is the *IMEs* period; (**c**) Interdigitated Nano Electrodes (*INEs*), where *w* is the width of the silicon layer, *g* is the gap between electrodes; and Λ*n* is the *INEs* period. The output signal of the Guided Mode Resonance (GMR) structure consists of the reflected optical signal (green arrows) by illuminating the optical section with *TE*-polarized light (red arrows).

The structure exploiting the *GMR* effect has been designed to obtain a resonance condition for *λ* > 800 nm, where the absorption losses of silicon decrease [41], with the aim of achieving a higher extinction ratio and higher *Q*-factor, together with a strong energy confinement close to the surface to enhance the light interaction with the bacteria. The strong refractive index contrast between silicon and the surrounding medium allows a high confinement of the electromagnetic field at the sensor surface, enabling the use of the *INEs* section for hyperspectral imaging technique, as described in detail in [42], which allows the refractive index imaging, thus localizing objects on the grating by detecting the spatial resonance distribution. An inverted microscope configuration can be used to characterize the sensor, with the light source illuminating from the top and the reflected signal collected from the same side of the setup by a conventional CMOS camera.

In order to work at the bacteria scale, we assume as a best compromise in terms of resolution and the large field of view an optical setup with an area of 1 mm2 and a spatial resolution down to few microns. This allows having a large area but still exploiting the advantages of near-field optics, in particular in terms of strong resolution. This approach has been experimentally validated in the literature with simple and feasible systems [42]. This behavior is also useful to obtain additional information about the first stage of the bacteria infection, when the bacteria cells enrich before they start to interact with each other to form the biofilm. During the biofilm formation, the cells produce extracellular polymeric substances (*EPS*), creating the surrounding matrix to protect the bacteria. The main *EPS* components are polysaccharides, proteins, lipids, and *DNA* with dimensions much smaller than the bacterial cells [43]. The real-time detection of the biofilm formation with label-free techniques is very challenging, mainly because the matrix generation begins when the first layers of bacteria close to the sensor surface are packed and arranged in large communities. Under this condition, the monitoring of the chemical and biological processes in these communities is difficult because the propagation length of the evanescent field, of the order of few hundreds of nanometers, corresponds only to the first layer of bacteria. Therefore, the optical approach is efficient for analyzing the first stages of the

biofilm formation when bacteria begin to form colonies, while a different approach should be investigated to clearly define the presence of a biofilm with its extracellular matrix. To meet this latter requirement, the *INEs* section has been designed to allow simultaneous optical and electrical measurements. In fact, several pairs of nanoelectrodes can be realized in an interdigitated configuration, by connecting in turn the doped silicon structures to two different metal electrodes, as shown in Figure 1c, so forming several capacitors at the nanoscale. This system of interdigitated electrodes can allow easy monitoring of the large change of the *PH* and the electrical properties of the solution induced by the secretion of proteins, *DNA,* and other *EPS* components when the bacteria colonies start to create the extracellular matrix [44]. In particular, the capacitance of the system changes when the bacteria start to grow because of the strong interaction between the electric field and the bacteria. Low-frequency values are usually used to detect changes of the system capacitance, because under this condition, the bacterial membrane behaves as a barrier to prevent the penetration of the electric field into the cytoplasm of the bacteria, which usually has a much higher conductivity, in order to make more evident any change of the impedance in proximity of the electrodes. The *INE* section supporting the *GMR* effect guarantees the confinement of the electric field close to its top surface [34], where the bacteria grow and the chemical processes happen, with an improved sensitivity compared to other electrical approaches with different systems and configurations.

Therefore, the *INEs* provide high spatial resolution for imaging few bacteria through an optical approach, to investigate the bacteria interaction to form colonies, and an electrical approach to analyze the biofilm evolution, due to the presence of *EPS* components that change the electrical properties of the solution, and, therefore, interfere with the electric field distribution. This interaction induces a change of the capacitance *Ci* of the nanocapacitor formed by each pair of nanoelectrodes, which depends on the electrical properties of the surrounding medium and the electrode geometry [44]. The net capacitance of the system, *Ctot*, is given by the sum of the single capacitances *Ci* in parallel combination.

A voltage of few millivolts and low frequency have been assumed, to have a low power interacting with bacteria with consequent strongly reduced risks, such as bacteria membrane collapses or changes of their metabolic state. An *AC* signal at low frequency, i.e., *f* 1 ≈100 Hz, is necessary to detect any change of the system capacitance [45]. The net impedance of the system is given by *Z* = 1/j*ωCtot* = 1/j*ωNCi*, where *N* is the number of nanocapacitors in the interdigitated configuration. This corresponds to a maximum value of current *imax* = *V*0/|*Z*| = *ωV*0*NCi*, which can be increased with the number of pairs of electrodes, leading to the increase in the Signal-to-Noise Ratio (*SNR*) and the sensitivity.

Since a higher sensitivity also corresponds to a faster saturation of the impedance change, due to the strong confinement of the electric field, which is not affected by the impedance changes in the upper layers of the biofilm, the main limitation of the *INEs* is given by the narrow dynamic range [46]. To monitor the biofilm upper layers, a great penetration depth of the electric field is needed, with a resulting large dynamic range and a lower sensitivity. For this reason, the sensor also includes an *IMEs* section placed next to the *INEs* structure (see Figure 1a). Since the penetration depth *L* of the electric field is proportional to the gap *G* and to the width *W* of the electrodes (*L*–*W*), features at the microscale lead to a large penetration depth and dynamic range [47]. In particular, by assuming *W* > 10 μm, any change of the metabolic state of the biofilm can be monitored as an impedance change. To improve the *SNR*, an *IMEs* driving voltage frequency *f* 2 larger than 1 kHz can be used, where the biofilm shows resistive behavior. According to the above-mentioned net impedance equation, an increase in the electrodes' driving voltage leads to a decrease in the impedance *Z*, allowing improvement in the accuracy of the detected changes in the biofilm with respect to the *INEs* section.

Although *INEs*- and *IMEs*-based biosensors have been already widely investigated, see as examples [48,49], we note that the main novelty of the proposed optoelectronic device is the combination of both optical and electrical approaches to perform on the same platform and at the same time the efficient monitoring of the bacteria growth and the

analysis of the resulting biofilm under antibiotics treatment. According to the above, it can be said that the high sensitivity is provided by *INEs* through a strong confinement of the electric field close to the top silicon surface, while the *IMEs* allow analyzing the growth and the maturation of the biofilm and studying its full or partial disruption induced by the antibiotic treatment for a complete, accurate, and real-time monitoring of the biofilm properties and analysis of *AMR* to specific antibiotics. The driving voltages *V*1 and *V*2 for *INEs* and *IMEs*, respectively, can be the same to simplify the setup of the system, while an *IMEs* driving voltage frequency *f* 2 larger than the *INEs* one is preferred to improve the SNR.

The system configuration could be realized following typical fabrication processes. For example, a lithographic process followed by reactive-ion etching can be used to define both the *INEs* and *IMEs* structures, while photolithography followed by metal evaporation and lift-off can be applied for the metal pads (Figure 1).

#### **4. Design of the Dual Array of Interdigitated Electrodes**

#### *4.1. Design of the INEs for Optical and Electrical Measurements*

The optical section, as already mentioned, consists of a *GMR* structure with a subwavelength grating in SOS technology. A doped silicon was assumed for doing electrical measurements, with a dopant concentration *ND* = 1021 cm−3 at the surface and an exponential drop-off to *ND* = 1018 cm−3 within 20 nm, which can be achieved by thermal diffusion doping [37,50]. This doping profile allows minimizing the optical losses, without any worsening of electrical performance. The geometrical features of the silicon subwavelength grating have been designed to enhance both electrical and optical performance. A resonance condition around *λ* = *λres* ≈ 850 nm is required to minimize optical losses due to the material absorption typical for silicon at lower wavelengths. To fulfill all the requirements, including fabrication tolerances, we have determined a thickness *t* = 270 nm, a period Λ*n* = 440 nm, and a fill factor *FF* = 0.5, corresponding to *w=g* = 220 nm (see Figure 2a). Furthermore, since the grating strength, and then the *Q*-factor, increases with the number of periods, thousands of periods have been assumed, without affecting the ability to detect the bacteria with a resolution of a few microns.

**Figure 2.** (**a**) Configuration of the *GMR* structure (*LB*: Lysogeny Broth); (**b**) Reflection spectrum; (**c**) Energy confinement at the resonance wavelength λ ≈ 841.5 nm.

The reflection spectrum of the grating has been calculated by the 3D Finite Element Method (*FEM*), assuming top out-of-plane excitation with *TE*-polarized and plane-wave collimated light (the electric field is oriented perpendicularly to the grating period direction).

Reflection spectrum and mode distribution at the resonance frequency are reported in Figure 2b,c, respectively.

The Lysogeny Broth (*LB)* (*nLB* = 1.333 + 5 × 10−7 i) has been assumed as the surrounding medium, which is a typical medium for bacteria, and the substrate is sapphire with *nsub* = 1.732. A resonance condition at *λres* ≈ 841.5 nm has been calculated. At *λres,* with the aforementioned doping profile, a refractive index of silicon *nSi* = 2.76 + 0.06 i at the surface results, which increases up to *nSi* = 3.648 + 4 × 10−3 i at 20 nm far from the surface. The resonance shows an amplitude higher than 0.8, which is normalized with respect to the

input power, and a Full Width at Half Maximum (*FWHM*) = 2.12 nm, which corresponds to *Q* ≈ 400. The proposed configuration represents the best compromise to achieve a high *Q*-factor, large modal confinement, and high resonance amplitude, which is useful for hyperspectral imaging and sensing.

The operation of the *GMR* structure in the presence of bacteria has been simulated by assuming a uniform and homogeneous layer of bacteria with a thickness *tlayer* of the order of hundreds of nm and refractive index *nbac*. For example, the *Escherichia coli* bacterium was considered, with a diameter *tbac* of 500 nm, a length *lbac* of 2 μm, and a refractive index *nbac* = 1.388 [51]. Since the length of the bacteria *lbac* is larger than the gap size *g*, the penetration of bacteria in the grooves is not allowed. Therefore, the grooves have been assumed to be filled by *LB* medium. The comparison between the 1D silicon *GMR* surrounded by *LB* only and the same structure with the presence of bacteria in solution is shown in Figure 3. A resonance shift Δ*λres* of about 0.9 nm in the case of the bacteria layer on the *GMR* structure, with a reflection change of about 0.02, was evaluated. This behavior confirms a high resolution in detecting the presence of the bacteria, even when the surface is not fully covered.

**Figure 3.** Reflection spectra for the case without bacteria (blue curve) and with bacteria (red curve) on the *GMR* structure. Spectra calculated by 3D FEM approach.

Since the evanescent field of the optical mode extends and interacts with the biofilm for a few hundred nanometers (<<*tbac* = 500 nm), beyond the first layer of bacteria, the optical response results are insensitive to an increase in the layer thickness, as confirmed by FEM simulations where the behavior of the reflected signal is the same for *tlayer* ≥ *tbac* with negligible resonance shifts. However, during the biofilm formation, a clearer resonance shift is also expected because of the release of small particles and molecules, possibly in the grooves, which would further affect the effective index. As an example, in the extreme case of filling the grooves with biomolecules secreted by the *Escherichia coli* bacteria (for which we assume the same refractive as for the bacteria, *nbac* = 1.388), 3D *FEM* simulations confirm a maximum resonance shift up to 7.5 nm. Therefore, in the presence of a biofilm, a final value of the resonance shift in the range 0.9 nm < Δ*λres* < 7.5 nm is expected.

When investigating the resonance shift in an array of several pixels, the resonance map for each pixel can give information about the position of bacteria and the size of colonies.

However, due to the short penetration length of the evanescent field in the surrounding medium, this approach cannot provide a very accurate analysis about the formation of a biofilm and its maturation. This limitation justifies the choice of a more complex biosensing platform by combining optical measurements with electrical ones. Hence, the same interdigitated silicon nanoelectrodes have also been used for electrical measurements, as shown in Figure 1a, where an applied voltage *V*0 = 10 mV enables the current flow *i*1. In order to not interfere with the optical response, the metal pads of this structure are far enough from the region used for optical measurements. An electrode length of about 1 mm fulfills this requirement, also avoiding any power absorption of the medal pads, with a consequent reduction of the interacting optical power. The main goal of the *INE* structure is detecting the presence of bacteria and the first stage of bacterial biofilm

formation. The maximum sensitivity can be reached by evaluating the changes of the system capacitance, where the capacitance is given by *C* = *εA*/*d*, with *ε* being the relative permittivity of the surrounding medium, *A* being the area of the electrodes, and *d* being the distance between the silicon surface and the charged particles released by the bacteria [52]. Electrical measurements require strong confinement of the electric field at the surface of silicon, which is achievable with a typical frequency *f* 1 ≈ 100 Hz. The relative permittivity of *LB* medium at *f* 1 = 100 Hz has been assumed to be equal to *ε* = 60, and the electrical conductivity *σLB* = 0.754 S/m. As the first step, the electrical behavior of the *INEs* has been evaluated by 2D *FEM* simulations without the presence of bacteria to observe the electric field distribution in the proximity of the electrodes. The grating length is much larger than the grating period and the electrodes features; therefore, we have assumed a 2D simulation as an optimal approximation of the real case of study. Figure 4 shows the distribution of the current density *J*1 [A/m2] without bacteria. As expected, the energy decreases as a function of the distance along the *z*-axis from the electrodes and increases with a peak of the energy density (≈7 × 10−4 A/m2) at the silicon ridges. This behavior confirms the suitability of the electrical measurements to monitor the biofilm at the silicon surface.

**Figure 4.** (**a**) Current density *J*1 [A/m2] distribution along the *x*-axis in the middle of the *INEs* structure with *f* 1 = 100 Hz, *V*0 = 10 mV, Λ*n* = 440 nm, and *FF* = 0.5; (**b**) Focus at the surface of the electrodes with the energy confinement in few hundreds of nanometers. Plots calculated by 2D FEM approach.

The total capacitance of the *INE* structure is directly proportional to the number of pairs of electrodes. A number of couples *N* = 2000 and a length of electrodes of the order of mm have been assumed in the model, which corresponds to a total width of the system of *N*·Λ = 2000 × 0.44 μm = 880 μm. The footprint of the order of mm2 (=880 μm × ≈1 mm) also guarantees a large area of the *INEs* section optimizing the optical reflection and obtaining more information for large bacterial colonies. The capacitance of a system with interdigitated electrodes is [53]:

$$C = \frac{N \cdot Q}{2V\_0} = N \cdot \frac{2}{V\_0^2} E = N \cdot \frac{2}{V\_0^2} \int\_{\Omega} \mathcal{W}\_c d\Omega \tag{1}$$

where Ω is the surrounding area of the electrodes close to their surface [m2], *E* is the energy for each pair of electrodes [J], and *We* is the electric energy density [J/m2]. Assuming *V*0 = 10 mV and *f* 1 = 100 Hz, the energy *E* is equal to 31 fJ, which corresponds to a total system capacitance *C* = 1.24 μF. In the low-frequency regime (*f* 1 = 100 Hz), the system behavior is capacitive, with a corresponding impedance *Zc =* 1*/jωC*, decreasing as *C* increases. *N* = 2000 corresponds to *Zc* = 1.2 kΩ. The maximum value of the current *i*1*,max* is given by [54]:

$$\dot{m}\_{1,\text{max}} = \max\left(\mathbb{C}\frac{\partial V}{\partial t}\right) = \max\left(\mathbb{C}\frac{\partial (V\_0 \sin(\omega t))}{\partial t}\right) = 2\pi f\_1 \mathbb{C}V\_0 = 7.79\text{ }\mu\text{A}.\tag{2}$$

To simulate the biofilm formation and maturation in the *LB*, the well-established Maxwell Mixture Theory (*MMT*) [55] has been used to define the electric properties of the biofilm. In particular, the *MMT* method assumes the biofilm as a compound of uniformly distributed spherical objects as the bacterial cells, which are covered by a shell to mimic the external membranes, forming the so-called Extracellular Matrix (*ECM*) [46]. With these assumptions, the dielectric permittivity and electrical conductivity of the biofilm can be theoretically estimated. In particular, the relative permittivity of the region with the biofilm *ε\*biofilm*(ω) is given by [55]:

$$
\varepsilon\*\_{\text{bi}\text{\#lim}}(\omega) = \varepsilon\*\_{\text{ECM}}(\omega) \frac{2(1-\varrho)\varepsilon\*\_{\text{mcl}} + (1+2\varrho)\varepsilon\*\_{\text{q}}(\omega)}{(2+\varrho)\varepsilon\*\_{\text{mcl}} + (1-\varrho)\varepsilon\*\_{\text{q}}(\omega)} \tag{3}
$$

where *ϕ* is the fractional volume of the bacterial cells in the *ECM* that has been assumed equal to 30% following a conservative approach [45],*ε\*MED* is the complex permittivity of the solution where the bacteria are immersed, and *ε\*eq*(*ω*) is the equivalent complex dielectric constant of the bacteria, expressed as [55]:

$$
\varepsilon\*\_{\varepsilon\eta}(\omega) = \varepsilon\*\_{mcm}(\omega) \frac{2(1-\theta)\varepsilon\*\_{mcm}+(1+2\theta)\varepsilon\*\_{cyt}(\omega)}{(2+\theta)\varepsilon\*\_{mcm}+(1-\theta)\varepsilon\*\_{cyt}(\omega)}\tag{4}
$$

with the complex permittivity of the *ECM ε\*ECM*(*ω*) = *εr\_ECM* + *σECM/(jε0ω)*, the complex permittivity of the bacterial cell membrane *ε\*MEM*(*ω*) = *εr\_MEM* + *σMEM/(jε0ω)*, and the permittivity of the bacterial cytoplasm *ε\*CYT*(*ω*) = *εr\_CYT* + *σCYT/(jε0ω)*. Moreover, *θ = (R/(R + d))*, with *R* and *d* being the radius of the bacteria and the thickness of the external membrane, respectively, and the parameters *ε0* and *ω* being the dielectric permittivity in vacuum and the angular frequency of the applied signal, respectively. The conductivity of the biofilm is calculated as *εbiofilm* = *εr\_biofilm + σbiofilm/(jε0ω)*. According to the *MMT* theory, a negligible change would be obtained with a model that assumes bacteria with an ellipsoidal shape instead of a spherical one. The parameters values used in the numerical model to implement the *MMT* are reported in Table 1. The electrical properties are derived by experimental measurements reported in the literature [46,56]

**Table 1.** Electrical properties of the parameters used in the *MMT* [46,56].

An initial value of the capacitance *C*0 = 1.24 μF has been simulated by assuming only *LB* medium above the nanoelectrodes. As already assumed in the optical analysis, the presence of a biofilm layer with a thickness of 1 μm, above the nanoelectrodes, was considered. Under this condition, the capacitance becomes *C'* = 1.41 μF, the capacitance relative change is Δ*C/C*0 = (*C'* − *C*0*)/C*0~14%, and the maximum value of current is equal to *i*1*,max* = 8.85 μA. This performance confirms the high sensitivity of the nanoelectrodes because a change of the current values of about 14% is obtained when a single layer of bacteria is placed on top of the nanoelectrodes (thickness = 1 μm). This behavior is strictly correlated to the strong confinement of the electric field at the surface of the electrodes and represents a significant advantage with respect to optical measurements in terms of sensitivity to the biofilm formation and maturation. However, the electrical measurement takes into account only an average change of the surrounding medium with a spatial resolution > 1 μm, while the optical approach provides the spatial distribution of bacteria along the grating with a much higher resolution, of the order of hundreds of nm, so demonstrating the strong complementarity of the methods. Since the strong electric field

confinement of the *INE* structure causes the saturation of the impedance value, even with a single layer of bacteria in the biofilm, negligible changes of the impedance for a thicker layer of biofilm were confirmed by *FEM* simulations. This restriction justifies the combination of the *INE* and *IME* structures in order to also detect a thicker biofilm for the study of the maturation phase and for the analysis of a possible biofilm disruption by using antibiotics whose results are challenging only with the *INE* structure.

#### *4.2. Design of the IMEs for the Detection of Biofilm Maturation or Disruption*

The same thickness and doping distribution of the *INEs* described in Section 4.1 were assumed for the configuration of the *IMEs* to realize the proposed array with a single manufacturing process. As above introduced, the *IMEs* function is providing an accurate analysis of the biofilm maturation and its possible disruption through an interaction of the electric field distribution with the upper layers of the bacterial biofilm. A design different from *INEs*, in terms of width *W*, gap *G* between the electrodes, and period Λ*m* (see Figure 1c) is required. The electric field distribution for different values of width *W* is in Figure 5, for different values of W (*W =* 5 μm, 10 μm, and 15 μm with *W=G*). The numerical results confirm that a larger value of *W* and *G* allows confining the electric field farther away from the electrodes, as required for the *IME* structure, at the expense of a decrease in the current density *J*2 [A/m2] due to a worsening of the related capacitance value.

**Figure 5.** Current density *J*2 [A/m2] distribution in the *IME* structures with a value of width *W=G* of 5 μm (**a**), 15 μm (**b**), and 25 μm (**c**). The current values have been normalized to the maximum value of current calculated for *W* = 5 μm. The dotted red line represents the distance from the electrodes, where 95% of the total energy is confined. Plots calculated by 2D FEM approach.

The performance of the *IME* configuration without the bacterial biofilm was defined by *FEM* simulations for different values of the width, assuming a number of electrodes *N* = 100, aiming at preserving the device compactness. Figure 6 shows the change of the capacitance for different values of *W* (assuming *G=W*) with respect to the capacitance *C*0 calculated when the *IMEs* are not covered by the biofilm layer.

The same model based on the *MMT* has been used to define the electrical performance for *IMEs*, as already proposed for the nanoelectrodes. The presence of multiple layers of bacteria has been considered for the biofilm, with each layer of bacteria assumed with a thickness of 1 μm.

As expected, the capacitance changes quickly for small gap values due to the stronger energy confinement, but this also corresponds to a more evident nonlinear behavior for a thickness of the biofilm of few microns (see Figure 6a), which makes the rigorous analysis of the biofilm behavior challenging, even with several layers of bacteria.

For example, a value of *G=W* = 5 μm presents a nonlinear behavior up to 5 μm, where the capacitance reaches its saturation value. For a biofilm thickness larger than 5 μm, the changes of impedance are negligible, making impossible the analysis of the upper biofilm layers. On the contrary, a larger value of *G* provides a more evident linear behavior of the capacitance change with respect to the thickness of the biofilm at the expense of less sensitivity. In fact, for *G=W=* 100 μm, a linear behavior of the capacitance has been

observed for a thickness of the biofilm of at least 15 μm. However, an impedance change of about 10% has been calculated for a thickness of the biofilm of 10 μm, while the impedance variation goes down to 5% with a thickness of 5 μm, which is six times lower than the performance obtained with *G* = 5 μm for the same biofilm thickness.

**Figure 6.** (**a**) Change of the capacitance Δ*C/C*0 *=* (*C'* − *C*0*)/C*0 in the *IME* structure for different values of thickness of the bacterial biofilm as a function of the electrode width (*W=G*); (**b**) Change of the capacitance Δ*C/C*0 as a function of the electrode width calculated for different values of the biofilm thickness.

Figure 6b confirms the nonlinear trend of the capacitance for smaller values of *G*, which becomes negligible with a larger *W*, in addition to a narrower dynamic range of the relative capacitance change Δ*C/C*0 that also corresponds to a lower sensitivity. The conditions *G=W=* 50 μm, and then, Λ*m* = 100 μm, have been chosen as the best compromise in terms of linearity and sensitivity, obtaining an impedance change up to 20% with a biofilm thickness of 15 μm. Since the detection of the changes of capacitance at the interface of the electrodes is not necessary, as required instead for the *INEs*, a frequency *f* 2 of 1 kHz has been assumed for the simulation of the current (*i*2 in Figure 1a) changes with different thickness of the biofilm in order to probe electrical changes in the bulk solution [57].

The electrical performance by varying the biofilm thickness is reported in Figure 7. A change of the current *i*2 from *i*2 = 1.76 up to 2.09 μA with a quasi-linear behavior has been calculated, enabling the detection of the current change for each layer of biofilm that confirms the ability to easily detect both the bacterial biofilm growth and maturation, together with its possible disruption caused by the action of the antibiotics. A similar behavior represents a significant improvement for *AMR* because an efficient, accurate, and real-time analysis of the bacteria interaction and useful information about their life in community and colonies can be achieved. Furthermore, the electrical measurements of the *IMEs* ensure an accurate analysis of the metabolic state of the biofilm during the whole process from the formation to the maturation and, possibly, the disruption for specific antibiotics.

**Figure 7.** Current *i*2 [μA] in the *IME* structure with *N* = 100, *f* 2 = 1 kHz, and *G* = *W* = 50 μm for different values of biofilm thickness. Plots calculated by 2D FEM approach.

#### **5. Discussions and Conclusions**

An innovative label-free biosensing platform based on a dual array of interdigitated electrodes for simultaneous optical and electrical measurements has been proposed for the analysis of bacteria and their interaction. A multiparametric approach for the *INEs* section offers high sensitivity to detect a very low concentration of planktonic bacteria and possibly down to a single cell, also ensuring the following of the biofilm formation in the initial stage. In addition, the electrical performance of the *IMEs* enables the monitoring of the growth and the maturation of the biofilm to possibly investigate the efficiency of the antibiotics. The overall performance, related to the merging of the micro and nano scale, outperforms the competitive technologies, whose operation is limited to monitoring a few biofilm formation stages. Therefore, the main advantage of the proposed system is the capability to detect and monitor in real time a biofilm, also analyzing its metabolic state and evolution phase. In its proof-of-concept form, the proposed optoelectronic device has been used for the monitoring of *Escherichia coli*-based biofilm, but the device could also be investigated to analyze biofilms formed by different strains and species of bacteria. Therefore, the proposed detection method's results are very promising due to high sensitivity, low-cost fabrication, and real-time operation, paving the way to a real-time and cost-effective solution to counteract the AMR phenomenon.

**Author Contributions:** Conceptualization, G.B., D.C., M.N.A. and C.C.; methodology, G.B. and D.C.; software, G.B. and D.C.; formal analysis, G.B. and D.C.; investigation, G.B. and D.C.; writing—original draft preparation, G.B.; writing—review and editing, D.C., M.N.A. and C.C.; supervision, M.N.A. and C.C.; funding acquisition, G.B. and C.C. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by POR Puglia FESR FSR 2014–2020–Action 10.4–"Research for Innovation" (REFIN) Initiative.

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

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

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

#### **References**

MDPI

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*Biosensors* Editorial Office E-mail<EMAIL_ADDRESS>www.mdpi.com/journal/biosensors

MDPI St. Alban-Anlage 66 4052 Basel Switzerland

Tel: +41 61 683 77 34 Fax: +41 61 302 89 18

www.mdpi.com

ISBN 978-3-0365-3972-0


*Edited by Monika Elzbieta Machoy*

The currently available technical and technological solutions of visualization in dentistry are a number of possibilities in creating images useful both for dentists and technicians. To gain a three-dimensional image of a patient's face and profile to visualize changes in appearance after applying a specific therapeutic method in the field of prosthodontics or orthodontics is no longer a problem. The same applies to a very accurate scan of the area to be treated - the exact map of the working field, on the basis of which the implementation of even very difficult dental procedures can be easily planned.  The book presents the newest means and capabilities of visualization in different fields of dentistry.

ISBN 978-1-83962-186-4

ISSN 2631-5343

Computer Vision in Dentistry

Published in London, UK © 2019 IntechOpen © Murmakova / iStock

## IntechOpen Book Series Biomedical Engineering, Volume 7


*Article* **Effect of Cr on Aqueous and Atmospheric Corrosion of Automotive Carbon Steel**

**Sang-won Cho 1, Sang-Jin Ko 1, Jin-Seok Yoo 1, Yun-Ha Yoo 2, Yon-Kyun Song 2 and Jung-Gu Kim 1,\***

**Abstract:** This study investigated the effect of Cr alloying element on the corrosion properties of automotive carbon steel (0.1C, 0.5Si, 2.5Mn, Fe Bal., composition given in wt.%) in aqueous and atmospheric conditions using electrochemical measurement and cyclic corrosion tests. Three steels with 0, 0.3, and 0.5 wt.% Cr were studied by electrochemical impedance spectroscopy. Polarization resistance (Rp) of 0.3 Cr and 0.5 Cr steels was higher than that of 0 Cr steel, and the Rp also increased as the Cr content increased. Therefore, Cr increases the corrosion resistance of automotive carbon steel immersed in a chloride ion (Cl−)-containing aqueous solution. In the cyclic corrosion test results, Cl− was concentrated at the metal/rust interface in all of the steels regardless of Cr content. The Cl− was uniformly concentrated and distributed on the 0 Cr steel, but locally and non-uniformly concentrated on the Cr-added steels. The inner rust layer consisted of β-FeOOH containing Cl− and Cr-goethite, while the outer rust layer was composed of amorphous iron oxyhydroxide mixed with various types of rust. FeCl2 and CrCl3 are formed from the Cl− nest developed in the early stage, and the pitting at CrCl3-formed regions are locally accelerated because Cr is strongly hydrolyzed to a very low pH.

**Keywords:** automotive steel; atmospheric corrosion; electrochemical impedance spectroscopy; cyclic corrosion test; iron oxide