**Ahmed Abdulhamid Mahmoud, Salaheldin Elkatatny \* and Dhafer Al Shehri**

College of Petroleum Engineering and Geosciences, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia<EMAIL_ADDRESS>(A.A.M<EMAIL_ADDRESS>(D.A.S.)

**\*** Correspondence<EMAIL_ADDRESS>Tel.: +966-5-9466-3692

Received: 17 January 2020; Accepted: 28 February 2020; Published: 2 March 2020

**Abstract:** Prediction of the mechanical characteristics of the reservoir formations, such as static Young's modulus (Estatic), is very important for the evaluation of the wellbore stability and development of the earth geomechanical model. Estatic considerably varies with the change in the lithology. Therefore, a robust model for Estatic prediction is needed. In this study, the predictability of Estatic for sandstone formation using four machine learning models was evaluated. The design parameters of the machine learning models were optimized to improve their predictability. The machine learning models were trained to estimate Estatic based on bulk formation density, compressional transit time, and shear transit time. The machine learning models were trained and tested using 592 well log data points and their corresponding core-derived Estatic values collected from one sandstone formation in well-A and then validated on 38 data points collected from a sandstone formation in well-B. Among the machine learning models developed in this work, Mamdani fuzzy interference system was the highly accurate model to predict Estatic for the validation data with an average absolute percentage error of only 1.56% and R of 0.999. The developed static Young's modulus prediction models could help the new generation to characterize the formation rock with less cost and safe operation.

**Keywords:** static Young's modulus; sandstone formations; machine learning

### **1. Introduction**

Prediction of the mechanical characteristics of the reservoir formations, such as Young's modulus (E), is necessary for the evaluation of the wellbore stability, reservoir compaction, hydraulic fracturing, and formation control [1]. E is a mechanical parameter that gives an indication of the resistance of the rock samples when exposed to a uniaxial load [2]. On the other hand, static Young's modulus (Estatic) is a critical parameter needed to build the earth geomechanical model [3]. It is also used for fractures' designing and mapping [4,5]. While drilling hydrocarbon wells, Estatic is also needed with other mechanical and petrophysical properties to make a full description of the in-situ stresses to ensure wellbore stability [6].

Estatic varies significantly with the change in lithology [2,7]. Estatic for shale ranges from 0.69 to 6.89 GPa. For limestone, it is between 55.16 and 82.74 GPa, and for sandstone, it is between 13.79 and 68.95 GPa [7]. These ranges confirm the wide difference in Estatic from one formation type to another and the huge change within the same lithology. Therefore, it is necessary to estimate Estatic along the whole drilled hydrocarbon well.

Two methods for rock elastic parameters' estimation are currently available. These are the experimental laboratory method or the use of empirical correlations. The experimental laboratory method is based on conducting laboratory experiments on the rock samples using static or dynamic testing techniques. In the static technique, the sample is subjected to a uniaxial or triaxial load and the deformation of the sample is measured, while in the dynamic technique, shear and compressional

wave velocities along the tested sample are measured and then the sample's elastic parameters are calculated based on the shear wave (Vs) and compressional wave (Vp) velocity [8]. In the field, wireline logging tools are used to measure Vs and Vp. The dynamic Young's modulus (Edynamic) can then be evaluated based on Vs and Vp and using Equation (1).

$$\mathrm{E\_{dynamic}} = \frac{\rho \mathrm{V\_S}^2 \left(3 \mathrm{V\_P}^2 - 4 \mathrm{V\_S}^2\right)}{\mathrm{V\_P}^2 - \mathrm{V\_S}^2} \tag{1}$$

where ρ denotes the formation's bulk density in g/cm3, VS and VP are in km/s, and Edynamic is in GPa.

Several previous studies confirmed that the laboratory-measured Edynamic for the same rock sample is significantly greater than Estatic [9–11]. Edynamic could be 1.5 to 3 times greater than Estatic [12] and some recent studies reported that Edynamic could be ten times greater than Estatic [13,14]. The strain amplitude between the two experimentally testing methods is the main reason for this huge difference, which decreases as the rock strength increase [15].

The static elastic parameters are actually representative of the in-situ stress–strain conditions of the reservoir [16]. Accurate determination of the static elastic parameters requires conducting a time consuming and costly experimental tests on real core samples [17]. The common practice to decrease this high cost is to select core samples at specific intervals and conduct the experimental tests of these cores only. Then an empirical correlation between the laboratory-derived parameters and the conventional well log data will be developed based on the results of laboratory tests. The static moduli throughout the whole reservoir depths can then be predicted by calibrating the dynamic moduli using the developed correlations [4]. Because of the heterogeneity of the reservoir formations, the developed well log-based empirical equations are usually not generalized to all formation types. Therefore, different correlations need to be developed for every formation type to track the changes in the static parameters along the whole reservoir.

The correlation in Equation (2) was developed by Fei et al. [18] for the evaluation of Estatic for sandstone formations; this correlation evaluates Estatic as a function of Edynamic, which was developed based on 22 triaxial tests results.

$$\text{E}\_{\text{static}} = 0.564 \,\text{E}\_{\text{dynamic}} - 3.4941 \,\tag{2}$$

where Estatic and Edynamic are in GPa.

Mahmoud et al. [19] developed a set of equations to estimate Estatic for different types of formations. The main advantage of the correlations developed by Mahmoud et al. [19] is the ability to implement these correlations directly to evaluate Estatic without the need for Edynamic, these correlations are only a function of the bulk formation density (RHOB), compressional transit time (DTc), and shear transit time data (DTs).

Different recent studies confirmed the ability of machine learning techniques to accurately estimate rock mechanical properties. Abdulraheem et al. [20] optimized three machine learning models of the artificial neural networks (ANN), fuzzy logic model, and functional neural networks (FNN) for estimation of Estatic and the static Poison's ratio for the hydrocarbon reservoirs. The authors did not specify the reservoir rock formation type. The developed models confirmed their ability to estimate the reservoir rock mechanical properties.

In another study, Tariq et al. [21] developed three machine learning models of ANN, fuzzy logic, and support vector machine (SVM) to estimate Estatic for limestone formation. The ANN model overperformed the other machine learning models and the currently available empirical correlation for Estatic estimation.

Tariq et al. [22] developed empirical correlations for the estimation of the mechanical properties of Estatic, Poisson's ratio, and unconfined compressive strength based on the application of the artificial neural networks (ANN) and the use of the conventional well log data, the authors also did not specify the type of the formation they used in this study. The developed correlations improved their ability to accurately estimate the rock mechanical properties.

In 2017, Parapuram et al. [23] developed an ANN model to estimate the geomechanical properties of the upper Bakken shale based on well log data. The results of this study confirmed the ability of the ANN model to accurately estimate the rock mechanical properties.

Recently, in our previous study, Mahmoud et al. [24], we evaluated the use of the ANN in estimating Estatic for sandstone formations. Mahmoud et al. [24] reported that ANN is able to predict Estatic with very high accuracy, and it overperformed all available empirical equations currently in use.

Sustainable development can be defined as development that meets the needs of the present without compromising the ability of future generations. This study is aimed at evaluating the ability of four machine learning techniques namely ANN, SVM, FNN, and the Mamdani fuzzy interference system (M-FIS) in estimating Estatic for sandstone formations as a function of RHOB, DTs, and DTc. The new systems of static Young's modulus prediction are examples of the new development which will help the new generation to discover and extract the oil and gas at lower cost and with safer operation. The developed method depends on taking the reading from the well logging tools and applying the artificial neural network models to predict the static Young's modulus and provide a continuous profile of the elastic property through the whole reservoir. This will improve the time necessary for the decision on the required action based on given information.

#### **2. Theory of Machine Learning Techniques Considered in This Study**

The first machine learning technique used in this work was the ANN, which is a computing system that is designed to mimic the way the biological systems, such as the human or animal brains, behave. ANN is developed to identify, estimate, classify, or make a decision by using a machine program. ANN is available in different structures; the simplest ANN structure, which was used in this study, is called multi-layered perceptron (MLP) which consists of one input layer, one or several hidden (learning) layers, and one output layer, as shown in Figure 1 [25]. The ANN systems are trained originally using training data (supervised learning) to perform the needed tasks [26].

**Figure 1.** Artificial neural networks model with input layers of three inputs, one hidden layer, and an output layer.

M-FIS was the second machine learning technique used in this study, which combines the adaptive neuro-fuzzy inference system (ANFIS) and subtractive clustering, where ANFIS is a multilayer feed-forward adaptive network in which the incoming signal will be subjected to a particular function performed by each training node where every node has its own parameters pertaining (Figure 2). The hybrid learning procedure was performed in two steps; the first step was the forward pass in which

the functional signals representing the input data go forward and the least square formula was used to identify the parameters in the output layer (layer 5). The second step was the backward pass, in which the error rates propagate, in the opposite way, and the gradient method was implemented to update the parameters in the input layer (layer 1) [27].

**Figure 2.** Adaptive neuro-fuzzy inference system architecture of a model using two inputs layers and one output parameter.

The subtractive clustering is an unsupervised clustering algorithm that aims to examine the density of the available input data. Then it defines the point surrounded by the highest number of neighbors as the cluster's center. It then subtracts (removes) the other data points within a pre-specified fuzzy radius, and the subtractive clustering algorithm considers only the point defined as the cluster's center. This process is repeated to examine all input data points. Subtractive clustering generates the rules that approximate a function [28].

The third machine learning technique used in this study was the FNN model, compared to the ANN which uses the sigmoidal common model. The FNN model works with the generalized functional models. In FNN, the neuron's function is learned from the existing data, which means they are not constant. Therefore, the weights related to links are not needed because the neuron functions include the effect of weights [29]. FNN contains an input layer, an output layer and layers of computing units that are related to each other. In FNN, there are different arguments in neural functions instead of one argument, such as in ANN [30].

The fourth machine learning model considered in this study was the SVM, which is one of the most famous classifying algorithms developed by Vapnik [31] in the framework of statistical learning theory. It performs classification of the data optimally into two or more divisions by applying a multidimensional hyperplane; this hyperplane is set to classify the data based on the tuning parameters (design parameters) of the kernel, regularization parameter (C), gamma, and margin. In its nature, SVM is very similar to a neural network, where the use of SVM with sigmoid kernel function is almost identical to the use of the perceptron neural network, having two hidden layers. Although it was originally developed in the statistical learning theory, the SVM technique is applicable in regression and classification problems, and it is also suitable for solving non-linear problems [32].

#### **3. Applications of Machine Learning in Petroleum Engineering**

Machine learning techniques are used in several scientific and engineering fields since the early 1990s to solve complicated non-linear problems. Petroleum engineers and petroleum geologists use different machine learning techniques to solve problems related to petroleum industry, such as the characterization of the heterogeneous hydrocarbon reservoirs [33,34], evaluation of the reserve

of unconventional reservoirs [35–38], estimation of the rock mechanical parameters, such as the static Poisson's ratio in carbonate reservoirs [39] and the static Young's modulus for sandstone reservoirs [24,40], evaluation of the integrity of wellbore casing [41,42], optimization of drilling hydraulics [43], evaluation of pore pressure and fracture pressure [44,45], hydrocarbon recovery factor estimation [46,47], determination of the alteration in the drilling fluids rheology in real-time [48,49], optimization of rate of penetration [50,51], prediction of the formation tops [52], and others.

#### **4. Application to the Well Log Data**

The predictability of the machine learning models depends on the amount of training data points and the design parameters of every model. In this work, the machine learning model's design parameters and the selection of the optimum training data points were conducted based on the optimization process of all combinations of the design parameters, as will be discussed in the following sections.


**3. Risk factors**

#### **3.1 ESA therapy**

Requirement of ESA for the treatment of anaemia in CKD is a major risk factor for haemoglobin variability. In a study involving 6,165 non-dialysis CKD patients, only 47% of patients who were not treated with any ESAs demonstrated fluctuations in haemoglobin (Boudville et al., 2009). However, 73% of patients who were already treated with ESAs experienced haemoglobin variability. This prevalence further increased to 77% among individuals who were commenced on ESAs as a new therapy. Patients treated with ESA therapy for a longer duration were less likely to have haemoglobin variability. Each 3-mo increment in the duration of ESA therapy decreased the risk of haemoglobin variability by 6%. In a study involving 5,037 ESKD patients on haemodialysis, the risk of developing haemoglobin variability was more than twice in patients on ESAs, compared to those not on ESAs (Eckardt et al., 2010). These findings suggest that the *need* of ESA rather than ESA therapy *per se* leads to haemoglobin variability (See Table 1).

There is increased interest in studying the effect of various anaemia management protocols on haemoglobin variability. Patel and colleagues studied the effect of route of administration of erythropoietin on haemoglobin variability in a post hoc analysis of a randomised controlled trial involving 157 prevalent ESKD patients on dialysis (Patel et. al., 2009). Over a follow up of 24-weeks, compared to patients treated with intravenous erythropoietin, those treated with subcutaneous erythropoietin were more likely to have (i)

2. Intra-individual standard deviation of 3-month haemoglobin rolling average (Lacson et.

3. Using 3 haemoglobin groups (above/below/within the target) for each of the 6 consecutive monthly haemoglobin values, classification of all patients into 6 groups: (i) consistently low, (ii) consistently high, (iii) consistently within the target range, (iv) low amplitude fluctuation with low haemoglobin values (all-6 mo with low or target-range haemoglobin values), (v) low amplitude fluctuation with high haemoglobin values (all-6 mo with high or target-range haemoglobin values), and (vi) high amplitude fluctuation with high haemoglobin values (low, high and target-range haemoglobin

5. Determining velocity (or deflection) of haemoglobin change by averaging slopes

6. Change in haemoglobin in 6-mo above and below the reference range (based on the

Haemoglobin variability is common not only in patients with end-stage kidney disease (ESKD) on dialysis, but also in CKD patients who are not yet on dialysis (non-dialysis CKD). The reported prevalence of haemoglobin variability in non-dialysis CKD patients varies between 61 to 86% (Boudville et. al., 2009; Minutolo et. al., 2009). On the other hand, 82 to 90% of ESKD patients on dialysis exhibit haemoglobin variability (Ebben et al., 2006;

Requirement of ESA for the treatment of anaemia in CKD is a major risk factor for haemoglobin variability. In a study involving 6,165 non-dialysis CKD patients, only 47% of patients who were not treated with any ESAs demonstrated fluctuations in haemoglobin (Boudville et al., 2009). However, 73% of patients who were already treated with ESAs experienced haemoglobin variability. This prevalence further increased to 77% among individuals who were commenced on ESAs as a new therapy. Patients treated with ESA therapy for a longer duration were less likely to have haemoglobin variability. Each 3-mo increment in the duration of ESA therapy decreased the risk of haemoglobin variability by 6%. In a study involving 5,037 ESKD patients on haemodialysis, the risk of developing haemoglobin variability was more than twice in patients on ESAs, compared to those not on ESAs (Eckardt et al., 2010). These findings suggest that the *need* of ESA rather than ESA

There is increased interest in studying the effect of various anaemia management protocols on haemoglobin variability. Patel and colleagues studied the effect of route of administration of erythropoietin on haemoglobin variability in a post hoc analysis of a randomised controlled trial involving 157 prevalent ESKD patients on dialysis (Patel et. al., 2009). Over a follow up of 24-weeks, compared to patients treated with intravenous erythropoietin, those treated with subcutaneous erythropoietin were more likely to have (i)

values within 6-mo period) (Ebben et. al., 2006). 4. Time-in-target haemoglobin range (De Nicola et. al., 2007).

Eckardt et. al., 2010; Gilbertson et. al., 2009).

therapy *per se* leads to haemoglobin variability (See Table 1).

between successive haemoglobin values (Lau et. al., 2010).

median change in haemoglobin in 6-mo) (Regidor et. al., 2006).

al., 2003).

**2.2 Prevalence**

**3. Risk factors 3.1 ESA therapy**

Table 1. List of possible factors affecting haemoglobin variability

haemoglobin concentrations outside the target range for more weeks (13.9 ± 4.7 weeks versus 12.5 ± 5 weeks, p=0.04) and (ii) higher standard deviation of haemoglobin (0.84 ± 0.35 versus 0.74 ± 0.27, p=0.01). Interestingly, in one report, the risk of developing haemoglobin variability was greater with long-acting ESAs (Boudville et al., 2009). De Nicola and colleagues did not find any association between long-acting ESAs versus erythropoietin and haemoglobin variability (De Nicola et al., 2007). However, they observed that baseline haemoglobin level, first dose of ESA and initial iron supplementation were directly associated with the length of time-in-target haemoglobin.

Depending on the 6-group classification based on the highest and lowest categories of haemoglobin, Gilbertson and colleagues reported that patients in the low-intermediate group received high doses of erythropoietin and more blood transfusions (Gilbertson et al., 2009). Minutolo and colleagues found that haemoglobin variability was associated with responsiveness to the first dose of erythropoietin (Minutolo et al., 2009). They also observed that a change of erythropoietin dosage occurred less frequently than expected in spite of regular follow up visits. Therefore, they concluded that lack of adjustment of erythropoietin dosage can lead to haemoglobin variability. However, their data on the effect of frequency and magnitude of adjustment of erythropoietin dosage on haemoglobin stabilisation is less clear. In a post hoc analysis of a randomised controlled trial involving 154 ESKD patients on haemodialysis, more frequent adjustments of erythropoietin dosage as well as larger changes of erythropoietin dosage were associated with haemoglobin variability (Lau et al., 2010).

Using data from the Dialysis Outcomes and Practice Patterns Study (DOPPS) involving 26,510 patients, Pisoni and colleagues studied the associations between facility-level

Association Between Haemoglobin Variability

haemoglobin (Lau et al., 2010).

**3.3 Facility-level factors**

generalised to an individual patient.

and Clinical Outcomes in Chronic Kidney Disease 33

catheter use and haemoglobin variability; and a negative association with high baseline

Weinhandl and colleagues studied the risk factors for haemoglobin variability in Medicare haemodialysis patients (Weinhandl et. al., 2011). The study cohort included 3 groups of haemodialysis patients: historical prevalent (prevalent on July 1, 1996; n=78,602), contemporary prevalent (prevalent on July 1, 2006; n= 133,246), and incident (January 1, 2005 - June 30, 2006; n=24,999). In both the prevalent groups, the presence of all comorbid conditions, except hepatic disease, was associated with greater haemoglobin variability. These conditions included atherosclerotic heart disease, congestive heart failure, arrhythmia and other cardiac diseases, cerebrovascular disease, peripheral vascular disease, cancer, chronic obstructive pulmonary disease, diabetes and gastrointestinal bleeding. In the incident group, the presence of cerebrovascular disease, peripheral vascular disease, chronic obstructive pulmonary disease, diabetes and gastrointestinal bleeding were associated with haemoglobin variability. In all 3 groups, cumulative hospital days and number of months with haemoglobin <10 g/dL were positively associated with haemoglobin variability. Similar findings have been reported by Gilbertson and colleagues (Gilbertson et al., 2009).

Pisoni and colleagues studied facility-level risk factors for haemoglobin variability in 26,510 haemoglobin patients from 930 facilities in 12 countries using the DOPPS data (Pisoni et al., 2011). Haemoglobin variability was not associated with the number of haemodialysis patients per facility. However, larger differences in mean facility-level haemoglobin standard deviation were seen between countries. The mean age was nearly 2 years younger in the highest quartile of facility-level haemoglobin standard deviation than the lowest quartile. The investigators found that BMI, neutrophil count, and prevalence of psychiatric disorders and hepatitis C were higher in facilities with higher facility-level haemoglobin standard deviation. They also observed a positive association between haemoglobin variability and increased proportion of patients in a facility with parathyroid hormone level >450 pg/mL. Furthermore, the investigators reported a strong correlation (*r* =0.56) between facility-level haemoglobin standard deviation and within-patient haemoglobin standard deviation. These findings suggest that the results of this facility-level study could be

An emerging body of evidence suggests that haemoglobin variability is associated with increased risk of all-cause death in both non-dialysis CKD and ESKD patients. Boudville and colleagues found an association between haemoglobin variability and death not only in non-dialysis CKD patients treated with ESAs (n=1,823), but also in those who were not on ESAs (n=3,143) (Boudville et al., 2009). For each 1 g/L increase in the residual standard deviation, HR (95%CI) for patients on ESA throughout the study and those who were not receiving ESA were 1.02 (1.01 to 1.04) and 1.03 (1.02 to 1.05), respectively. The analysis of the pooled data from these 2 groups showed similar results. Compared to patients with haemoglobin levels consistently within the target range, those with low amplitude fluctuation with low haemoglobin values (HR 1.62, 95%CI 1.36 to 1.94) and high amplitude fluctuation (HR 1.57, 95%CI 1.24 to 1.98) were at increased risk of all-cause mortality.

**4. Association between haemoglobin variability and mortality**

anaemia management practices and facility-level haemoglobin standard deviations (Pisoni et. al., 2011). This study identified factors that decreased haemoglobin variability which include reviewing ESA dose at least twice a week and checking haemoglobin levels on a weekly basis. There was also less haemoglobin variability in facilities with a greater percentage of patients prescribed an ESA likely related to better anaemia management with the introduction of an ESA and fewer patients outside the target haemoglobin concentration. The factors that were more likely associated with haemoglobin variability were: facilities with a wider target haemoglobin range, higher upper target haemoglobin, and ESA administration by subcutaneous route (compared to intravenous route).


**1. Introduction**

200 Soybean – Genetics and Novel Techniques for Yield Enhancement

Sinclair, T.R., Purcell, L.C., King, C.A. , Sneller, C.H., Chen, P. & Vadez, V. (2007). Drought

Sponchiado, B.N., White, J.W., Castillo, J.A. & Jones, P.G. (1989). Root growth of four

Udensi, O., Ikpeme, E.V., Markson, A.A., Edu, E.A.B., Umana, E.J. & Urua, I.S. (2010).

Umezawa, T., Sakurai, T., Totoki, Y., Toyoda, A., Seki, M. & Ishiwata, A. (2008). Sequencing

Valliyodan, B. & Nguyen, H.T. (2008). Genomics of abiotic stress in soybean. In: *Genetics and Genomics of Soybean*, G. Stacey, (Ed.), 343–372, Available from Springer, New York Van Heerden, P.D.R., Kiddle, G., Pellny, TK., Mokwala, P.W., Jordaan, A., Strauss, A.J., de

*Physiology*, Vol. 148, No. 1, (September 2008), pp. 316-327, ISSN 0032-0889 Vodkin, L.O., Khanna, A., Shealy, R., Clough, S.J., Gonzalez, D.O. & Philip, R., Zabala, G.,

Warren, C.R. (2008). Stand aside stomata, another actor deserves centre stage: the forgotten

Xia, Z., Tsubokura, Y., Hoshi, M., Hanawa, M., Yano, C. & Okamura, K. (2007). An

59, No. 7, (May 2008), pp. 1475-1487, ISSN 0022-0957

*of Current Research,* Vol. 7*,* (August, 2010) pp. 5-8*,* ISSN 0975-833X

4290

257, ISSN 0014-4797

ISSN1340-2838

2164

ISSN1340-2838

tolerance and yield increase of soybean resulting from improved symbiotic N fixation. *Field Crops Research,* Vol. 101, No.1, (February 2007), pp. 68–71, ISSN 0378-

common bean cultivars in relation to drought tolerance in environments with contrasting soil types. *Experimental Agriculture,* Vol. 25, No. 2, (April 1989), pp. 249-

Selection of soybean genotypes using morphological markers. *International Journal*

and analysis of approximately 40,000 soybean cDNA clones from a full-lengthenriched cDNA library .*DNA Research,* Vol. 15, No. 6, (December 2008) pp. 333-346,

Beer, M., Schlüter, U., Kunert, K.J. & Foyer, C.H. (2008). Regulation of respiration and the oxygen diffusion barrier in soybean protect symbiotic nitrogen fixation from chilling-induced inhibition and shoots from premature senescence *Plant*

Thibaud-Nissen,F., Sidarous, M., Strömvik, M.V., Shoop, E., Schmidt, C., Retzel, E., Erpelding, J., Shoemaker, R.C., Rodriguez-Huete, A.M., Polacco, J.C., Coryell, V., Keim, P., Gong, G., Liu, L., Pardinas, J. & Schweitzer, P. (2004). Microarrays for global expression constructed with a low redundancy set of 27,500 sequenced cDNAs representing an array of developmental stages and physiological conditions of the soybean plant. *BMC Genomics,* Vol. 5, (September 2004), p. 73, ISSN 1471-

role of the internal conductance to CO2 transfer. *Journal of Experimental Botany,* Vol.

integrated high-density linkage map of soybean with RFLP, SSR, STS, and AFLP markers using a single F2 population. *DNA Research,* Vol. 14, No. 6, pp. 257–269, Soybean [*Glycine max* (*L.*) Merr.,] is considered a high quality source of oil and protein for food and feed. However, the several antinutritional factors ( lipoxygenase, trypsin inhibitor, lectin, and P34 allergen protein) present in raw mature soybean seeds. Soybean Kunitz trypsin inhibitor (KTI) protein has been proposed as one of the major antinutritional factor (Westfall and Hauge, 1948). KTI protein is a small, monomeric and non-glycosylated protein containing 181 amino acid residues. This 21.5 kDa non-glycosylated protein was first isolated and crystallized from soybean seeds by Kunitz (1945). KTI protein can cause the induction of pancreatic enzyme hypersecretion and a fast stimulation of pancreas growth, which is histologically described as pancreatic hypertrophy and hyperplasia (Liencer, 1995). Also, KTI may cause unfavorable physiological effects (Vasconcelos et al., 2001) and decrease weight gain in animals (Palacios et al., 2004). Proper heat processing is required to destroy KTI protein. However, excessive heat treatment may lower amino acid availability. The genetic removal of the KTI protein will improve the nutritional value of soybean. From the USDA germplasm collection, two soybean accessions (PI157440 and PI196168) lacking the KTI protein have been identified (Orf and Hymowitz, 1979). Based on the availability of soybean null lines lacking the KTI protein, it was suggested that KTI protein is not essential for soybean growth or development. Five electrophoretic forms of KTI have been discovered. The genetic control of four forms, *Ti a*, *Ti b*, *Ti c*, and *Ti d*, has been reported as a codominant multiple allelic series at a single locus (Singh et al., 1969; Hymowitz and Hadley, 1972; Orf and Hymowitz, 1979). Orf and Hymowitz (1979) found that the fifth form does not exhibit a soybean trypsin inhibitor-A2 band and is inherited as a recessive allele designated *ti*. Studies of amino acid and nucleotide sequences of polymorphic variants of KTI have revealed that there is a large sequence differences in nine amino acid residues between *Ti a* and *Ti b* (Song et al., 1993; Wang et al., 2004). Each *Ti c*, *Ti d* and *Ti e* differ by only one amino acid from *Ti a* type and *Ti f* differs by one amino acid from *Ti b* type (Wang et al., 2004). The *Ti* locus has been located on linkage group 9 in the classical linkage map of soybean (Hildebrand et al., 1980; Kiang, 1987), which is integrated in molecular linkage map A2 (chromosome number 8) of the USDA/Iowa State University soybean molecular linkage map (Cregan et al., 1999).

Identification and Confirmation of

**2.4 Genetic linkage analysis**

**2.5 Detection of Satt228 marker**

recessive gene.

**2.3 DNA extraction and DNA marker analysis**

population 1 including *Ti* locus were used in population 2.

using the Kosambi (Kosambi, 1944) mapping function.

SSR Marker Tightly Linked to the Ti Locus in Soybean [*Glycine max* (L.) Merr.] 203

F2 seeds tested for kunitz trypsin inhibitor protein were planted in the field on May, 2004. Young leaves were collected from the 94 individual F2 plants germinated among 98 F2 seeds and parent plants in population 1. In population 2, random 97 F2 seeds among 243 F2 seeds were planted in the greenhouse on April, 2005. Young leaves were collected from the 94 individual F2 plants. Genomic DNA was extracted from finely ground leaf tissue by means of a modified CTAB procedure (Saghai Maroof et al., 1984). For the analysis of random amplified polymorphic DNA (RAPD) markers, One-thousand 10-mer oligonucleotide primers were obtained from Operon Technologies (Alameda, U.S.A). For the analysis of simple sequence repeat (SSR) marker, total 35 SSR primers were selected from the A2 soybean molecular linkage map (Cregan et al., 1999) that contains *Ti* locus. Satt primers selected were synthesized by Bioneer, Inc. (Korea). For the analysis of amplified fragment length polymorphic (AFLP) markers, 342 primer sets were used. Amplification and electrophoresis for RAPD, SSR, and AFLP markers was performed as described by Kim et al., (2003). Based on the results of F1 seed genotype for kunitz trypsin inhibitor, the present and absent bulk populations from F2 plant population were made (Michelmore et al., 1991). The present and absent bulk population contained twenty F2 individuals each, which were selected on the basis of the kunitz trypsin inhibitor protein electrophoresis, respectively. RAPD, SSR, and AFLP markers were used in population 1. Only the markers linked in

Primers that distinguished the bulks and the parents were tested on the entire F2 population. Marker (RAPD, AFLP, and SSR) data obtained from 94 F2 progenies of population 1 and 2 were used to construct genetic linkage map including *Ti* locus using the computer program MAPMAKER v. 3.0 (Lander et al., 1987). Markers were assigned to group using the "Group" command, with a LOD score of 4.0 and maximum recombination distance of 50 cM. Once markers were assigned to a given linkage group, the most linkage marker order within the group was determined using the "Compare" command. Marker orders within each linkage group were ascertained by use of "Ripple" command. Map distance (cM) were computed

The banding patterns of kunitz trypsin inhibit protein (SKTI) that appeared in the parents and F2 seeds from the cross between cultivar Jinpumkong2 and C242 (population 1) are shown in Figure 1. Jinpumkong2 parent had band in 21.5 KDa position and the band was segregated in F2 seeds. The observed data for population 1 were 72 seeds with SKTI protein band and 26 seeds with no SKTI protein band (χ2=0.12, P=0.70-0.80). For population 2, the observed data were 185 seeds with SKTI protein band and 58 seeds with no SKTI protein band (χ2=0.17, P=0.70-0.80). These observations fit the expected 3 : 1 ratio for the presence or absence of the SKTI protein band. Earlier studies have shown that the null phenotype of SKTI is inherited as a recessive allele designated *ti* (Orf and Hymowitz, 1979). The segregation ratios of 3 : 1 observed in the F2 seed (population 1 and 2) and the Chi-square values strongly suggest that kunitz trypsin inhibitor protein band is controlled by a single

DNA markers have become fundamental tools for research involving soybean improvement programs. Microsatellites or simple sequence repeat (SSR) markers are highly polymorphic, abundant, and distributed throughout the genome (Cregan et al., 1999). With the development and public release of SSR primers, SSR markers have become available on molecular soybean linkage group (Cregan et al., 1999). Molecular markers tightly linked to desired genes are a valuable tool to detect genotypes of interest, saving time and resources. Marker assisted selection (MAS) using DNA markers instead of phenotypic assays reduces cost and increases the precision and efficiency of subsequent selection steps applied in breeding. To date, detection of the KTI protein free genotypes has been based on SDS-PAGE gel electrophoresis analysis of crude protein from mature seeds, however, with this method, test samples are restricted to proteins from mature soybean seeds. This is a time-consuming process, which is not possible in the early seedling stages of the corresponding population. SSR markers tightly linked to the *Ti* locus were identified and confirmed in soybean populations for marker assisted selection. If a marker linked to the *Ti* locus can be confirmed, then selection for KTI protein free genotypes might be performed at early seedling stages with relative ease.


**1. Introduction**

Fluorescence imaging is a sensitive and noninvasive method for investigating physiological and biomolecular processes in vitro and in vivo. Tuning the excitation light to 650–900 nm (red/near infrared (NIR)) has made fluorescence imaging in centimeter-deep tissue possible [1]. The red/NIR region is suitable for deep in vivo imaging as water and hemoglobin have their lowest absorption, while tissue autofluorescence and scattering are relatively low [1–4]. Despite the advantages that red/NIR imaging confers, there are a limited number of fluorescent dyes with excitation and emission in this region. Indocyanine green (ICG), one of the cyanine family dyes, remains the only FDA-approved NIR dye currently administered for clinical applications [1].

Environment-sensitive fluorescent probes are capable of responding to changes in the immediate microenvironment. By changing emission characteristics in response to stimuli or cellular conditions such as polarity, pH, viscosity, hypoxia, ions, etc. [5,6], these probes often forewarn of a severe disease (e.g., higher blood viscosity in diabetic patients [5,7] and lower pH and hypoxia in the tumor tissue of cancer patients [8]).

Among the environmental stimuli, polarity is an important stimulus associated with hydrophobicity of proteins and consequently a broad range of diseases such as Alzheimer's, in which elevation of hydrophobicity is concurrent with the increase in aggregation-prone proteins [9]. In addition to conveying vital information about the degree of aggregation of proteins, polarity-sensitive fluorescent probes have major applications in the synthesis of thermosensitive switches in conjunction with thermosensitive polymers (Figure 1a).

Although temperature-sensitive fluorescent probes have been reported for numerous applications, they suffer from low sensitivity. Rhodamine-B, a widely used probe for thermometry, has a mild thermosensitivity of 2.3% per degree Kelvin [10]. Multicolor methods, on the other hand, have not attained sensitivities of more than 10% [10]. By contrast, polarity-sensitive fluorescent probes, in conjunction with thermoresponsive polymers, show a dramatic change in fluorescence intensity in response to the change in polarity and, consequently, temperature.

Recently, a new polarity-sensitive fluorophore was introduced, and its characteristics and application as a switchable fluorescent probe (SFP), when incorporated inside polymeric nanoparticles, were studied [11]. In this system, the heat generated from the focused ultrasound would increase the temperature of the tissue at the focus of the ultrasound. Increasing the temperature to above the lower critical solution temperature (LCST) of the probes would elicit a response from the thermoresponsive polymer. The shrinkage of the nanoparticle would affect its water content, and consequently decrease the polarity of the immediate microenvironment of the fluorophores. This would trigger the fluorophore molecules to switch from an "OFF" or dark state, at which they emit weakly, to an "ON" state, at which they emit strongly.

In pursuit of more versatile and stronger environment-sensitive probes, herein, a set of aza-BODIPY-based and structurally related fluorophores were investigated, and their polarity and/or viscosity sensitivity, as well as the structural characteristics leading to such behavior, were explored. The response of the fluorophores was also tested when embedded inside Pluronic nanoparticles (Figure 1a,b).

**Figure 1.** (**a**) Nanoparticles made of thermoresponsive Pluronic block copolymers, embedded with polarity-sensitive fluorophores, respond to the increase in temperature. By shrinking and excreting the water from the core of the nanoparticles, polarity is significantly decreased and polarity-sensitive fluorophores switch to "ON." (**b**) Pluronic F-127, F-98, and F-68 triblock copolymers with hydrophobic and hydrophilic chains [12,13]. (**c**) A schematic of the optical measurement system.

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

## *2.1. Fluorophore Characteristics*

The optical properties of six aza-BODIPY-based and structurally related fluorophores were investigated in regard to their structure as provided in Figure 2. All fluorophores were synthesized by Dr. D'Souza's team. The synthesis methods for fluoprophores 1, 3, 4, 5 and 6 have been reported previously [2,14–16]. The synthesis method for fluorophore 2 is provided in Supplementary Materials S4. The fluorophores' names, excitation wavelength (λex), and detection emission wavelength (λem) are summarized in Table 1. The fluorescence spectra are provided in Supplementary Materials S1.

**Figure 2.** Structure of the aza-BODIPY-based and structurally related fluorophores (synthesis methods have been previously reported for fluorophores 1, 3, 4, 5, 6 [2,14–16] and for fluorophore 2 in Supplementary Materials S4).

**Table 1.** Summary of the fluorophores' names, excitation, and emission detection wavelengths adopted in the experiments [2,14–16] and Supplementary Materials S4.

#### *2.2. Fluorescence Measurement System*

The fluorescence measurement system utilized for lifetime and intensity measurements was discussed in previous work [17,18], with minor changes to accommodate the temperature control system and corresponding filters for di fferent fluorescent dyes. Briefly, a combined laser system from Optical Building Blocks Corporation (Birmingham, NJ, USA) generated an 800 ps pulse at the excitation wavelength of each dye. All lenses used in the system were purchased from Thorlabs Inc. (Newton, NJ, USA), if not otherwise stated. The generated fluorescence light passed through a converging lens, and the corresponding emission band-pass or long-pass filter (BP, FF01-711/25-25, FF01-785/62-25, or BLP01-808R-25 from Semrock, Rochester, NY, USA). The emitted fluorescent light was detected by a photomultiplier tube (PMT, H10721-20, Hamamatsu, Japan). A pulse delay generator (PDG, DG645, Stanford Research Systems, Sunnyvale, CA, USA) triggered the 2.5 GHz oscilloscope (DPO 7254, Tektronix, Beaverton, OR, USA). The output of the PMT was converted to a voltage signal and was amplified by a broadband preamplifier (C5594, bandwidth from 50 kHz to 1.5 GHz, Hamamatsu, Japan). The signal was ultimately acquired by the multichannel and broadband oscilloscope. Each emission decay pulse recorded from the oscilloscope, used for calculating fluorescence lifetime, was averaged 100 times. A temperature controller (PTC10, Stanford Research System, Sunnyvale, CA, USA) was used to control the temperature (Figure 1c).

#### *2.3. Fluorescence Emission Intensity and Lifetime Measurement*

Since the fluorescence signal is acquired by an oscilloscope, the peaks of the decay curves indicate the voltage, and are proportional to the fluorescence intensity. For lifetime calculations, the acquired signal and the impulse response function were deconvolved and the decay curve was fitted to a monoexponential decay function [18]. Calculations were done using MATLAB (Natick, MA), with an iterative method to find the best fit with lowest residue. For fluorescence intensity calculations in polarity experiment, after averaging the emission curves 100 times, a moving average filter (n = 5) was applied, and the peak height of the decay curve was obtained in each solvent for all fluorophores. Fluorescence intensity of each fluorophore in water was normalized to 1. For fluorescence intensity calculations in fluorophore-encapsulated Pluronic nanoparticles, the same protocol was followed without a moving average filter. Herein, the peak voltage of the decay curve at each temperature was obtained (mV), and the switching ratio was calculated as discussed in the text. For comparisons regarding the Pluronic nanoparticles, a two-tail *t*-test (equal variance) was conducted. Pearson linear correlation coe fficient was calculated with R program.

#### *2.4. Solvents with Di*ff*erent Polarities*

Fluorescence lifetime and emission intensity of the fluorophores were measured in di fferent solvents. Solvents were obtained from Sigma-Aldrich Corporate (St. Louis, MO, USA), if not otherwise stated. Characteristics of the solvents used are provided in Table 2. The final concentration of fluorophores was kept at 50 nM.

**Table 2.** Solvent properties [19–21].

#### *2.5. Solvents with Di*ff*erent Viscosities*

To cover a range of viscosities, di fferent mixtures of ethylene glycol (EG) and glycerol (Gl) were prepared. Solutions were prepared by mixing Gl and EG at di fferent volume ratios: Gl/EG (*v*/*v*) % of (0/100)%, (16/84)%, (50/50)%, (75/25)%, (92/8)%, and (100/0)%. The viscosity of the mixtures (Table 3) were measured with an A&D SV-10 Viscomter (Tokyo, Japan).

**Table 3.** Viscosities pertaining to Gl/EG (*v*/*v*) % solutions.

#### *2.6. Preparation of Fluorophore-Encapsulated Pluronic Nanoparticles*

Nanoparticles were synthesized based on a revised protocol from Pluronic block copolymers consisting of poly(ethylene oxide) (PEO) and hydrophobic poly(propylene oxide) (PPO) [22]. Pluronic F-127 (PEO100-PPO65-PEO100) [12] obtained from Sigma-Aldrich (St. Louis, MO, USA), as well as Pluronic F-98 (PEO118-PPO45-PEO118) [13] and Pluronic F-68 (PEO80-PPO30-PEO80) [13] obtained from BASF (Florham Park, NJ, USA), were dissolved in deionized water (D.I. water) at pH 8.5 with a 5% (*w*/*v*) ratio with stirring. The fluorophores and tetrabutylammonium iodide (TBAI) from Sigma-Aldrich (St. Louis, MO, USA) were dissolved with a molar ratio of 1:8 in 6 mL chloroform (Fisher Scientific, Pittsburgh, PA, USA) by sonication using a bath ultrasonic cleaner (Branson 1510, Branson Ultrasonic Corporation, Danbury, CT, USA) and added drop-wise to 15 mL of the Pluronic solution while stirring at 1200 rpm. The sample was then sonicated with an XL-2020 probe-sonicator (Misonix, Farmingdale, NY, USA), while the probe intensity was kept at ~5–5.5. The sample was then moved to a beaker covered by aluminum foil with generated holes and stirred at 200–300 rpm overnight to evaporate the chloroform (final concentration of fluorophores was kept at about 50 μM). Nanoparticles were then filtered with (10,000 MWCO) Amicon ultra centrifugation filters (Merck Millipore, Billerica, MA, USA) and large particles and/or impurities were filtered by 0.45 μm membrane filters (Fisher Scientific, Pittsburgh, PA, USA). The e ffect of filtration and dilution on LCST has been extensively studied in the Supplementary Materials S3. For filtration, samples were diluted 5 times (for facilitating the process), centrifuged at 4500 G with a Legend X1 centrifuge (Sorvall ™ Legend ™ X1, Thermo, Marietta, OH, USA), reduced, and then brought back to the initial volume with D.I. water. All measurements were done with filtered 1% samples, except for when the e ffect of filtration was studied, where 1% and 0.2% samples, with and without filtering, were prepared.

#### **3. Results and Discussion**

#### *3.1. Response to Polarity*

To investigate the e ffect of polarity, the fluorescence intensity of the fluorophores, were measured in di fferent solvents. By changing the solvents, from water to toluene, the polarity of the microenvironment changed 28.9 units of polarity index, ET (30). This polarity index was defined as the molar electronic transition energy of the negatively solvatochromic pyridinium N-phenolate betaine dye as probe molecule, measured in kilocalories per mole (kcal mol−1) at room temperature (25 ◦C), and normal pressure (1 bar) [23]. Normalized fluorescence intensity vs polarity index is plotted in Figure 3. The decay curves are provided in Supplementary Materials S2. The maximum emission peak for each fluorophore among solvents is referred to as Imax, and the emission peak in water is denoted as Iwater.

As demonstrated in Figure 3, based on the normalized fluorescence intensity values, ADP(OH)2 Bottom (i.e., fluorophore 4) had the strongest dependency on the polarity with Imax-to-Iwater ratio of ~478, followed by Top (OH)2 ADP (i.e., fluorophore 5) with a ratio of ~9.58. Top Dimethyl amine ADPCNCA (TOP DMAADPCA, i.e., fluorophore 2) and Top Dimethyl amine ADPF2 (TOP DMA ADP, i.e., fluorophore 1) had a ratio of 3.77 and 5.04 times, respectively. ADP Di Sulphonic acid (ADP Di

(SA), i.e., fluorophore 6) had a ratio of 3.8 times, followed by Benzannulated ADPF2 (i.e., fluorophore 3) that had the lowest ratio of 2.16 times. A previous publication reported that Top Dimethyl amine ADPF2 (TOP DMA ADP, i.e., fluorophore 1) has polarity sensitivity between 677 and 692 nm [14]. Because of the particular interest in the red/NIR region, we measured the polarity sensitivity between 723 and 847 nm, in this study.

**Figure 3.** Peak fluorescence emission signal intensity vs solvent polarity. Power function fitted to the points depicted with black squares. The fluorescence intensity in water is normalized to 1.

The significant decrease in the fluorescence intensity from the highest polarity value, observed for fluorophores 3, 4, 5 and 6 in benzene and toluene (the last two squares) might be due to lower solubility in highly nonpolar solvents. Since we are only interested in the response to polarity, the points in which the fluorescence intensity was affected by solubility were shown with hollow squares and not included in fitting. To limit the effect of solubility, very low concentrations (50 nM) were used, and no aggregation was observed. In general, the fluorescence emission intensity of all the fluorophores increased with the decrease in polarity. The existence of amine groups attached to the fluorophore system seems unnecessary for generating a strong polarity-sensitive probe. As such, fluorophore 4 is a highly sensitive probe, despite not having an amine group.

In the case of fluorophores with substitutions of electron-donating groups to the aryl rings attached to the core of the fluorophore, polarity sensitivity was most prominent when the hydroxyl groups were attached to the para position of aryls at 3- and 5- (fluorophore 4), and to a lesser degree, when hydroxyl groups were attached to the meta position of the aryls at 1- and 7- (fluorophore 5), or when the sulfonate groups were attached to the aryls at 3- and 5- positions (fluorophore 6). More conclusive results were obtained by synthesizing Pluronic nanoparticles embedded with the fluorophores.

#### *3.2. Response to Viscosity*

The fluorescence intensity and lifetime of the fluorophores were measured in regard to the viscosity of the environment, while polarity was kept relatively constant (below 3.2 units of ET (30)). To quantitatively investigate the relationship between the fluorescence intensity with the viscosity, the Förster–Hoffmann equation was used [24,25]:

$$
\log \text{(I)} = \text{C} + \lambda \log(\text{(I)}) \tag{1}
$$

Here, I is the peak emission intensity, ɳ is the solvent viscosity, C is a constant related to temperature and concentration, and X is a constant related to fluorophore properties. For each fluorophore, the logarithm of signal peak in EG (lowest viscosity) was normalized to 1. As evident from the graphs (Figure 4), the fluorescence intensity of fluorophores 2, 4, 5, and 6 was relatively insensitive to viscosity. The fluorescence intensity of ADP BF2 (fluorophore 3) dropped in the viscous medium, while the fluorescence intensity of fluorophore 1 increased about 14 times at the beginning, and then decreased. The decrease in fluorescence intensity could be due to lack of a response to viscosity (fluorophore 3), or a response (fluorophore 1) compounded by the decrease in fluorescent emission, caused by the change in polarity in this experiment. It should be noted that by changing the composition of the mixture, polarity changed 3.2 units of ET (30), and inevitably the response to viscosity was convolved with the response to polarity. Although our result does not refute the fact that loss of energy due to rotational motion happens, it implies that viscosity sensitivity is not a major contributor to the phenomenon under study. Our data is consistent with previous studies reported in the literature that phenyl substitutions at 3- and 5- positions cannot rotate freely as a result of the steric hindrance caused by interaction of the hydrogen in the C-H bond with the fluorine atom from the BF2 [26,27].

**Figure 4.** Fluorescence intensity in response to the change in the viscosity.

#### *3.3. Fluorophore Encapsulation in Pluronic Nanoparticles*

Based on the polarity sensitivity results, while all fluorophores responded to the change in polarity, it was concluded that the attachment of amine groups to the fluorophore was not necessary for having strong polarity-sensitive probes. For the next step, the fluorophores were encapsulated into Pluronic F-127 nanoparticles, and their response to temperature was measured with increasing the temperature (Figure 5). Fluorophore 4 was also encapsulated in Pluronic F-98 and F-68 nanoparticles, to investigate the effect of hydrophobic and hydrophilic chain lengths of Pluronic nanoparticles.

As shown in Figure 5, when the temperature is below the LCST (lower critical solution temperature) of the nanoparticles (black arrow), the fluorescence emission is very weak because the fluorophores in the nanoparticles are exposed to a microenvironment in which water molecules are rich, and therefore, the polarity is high and thus, the fluorophore has a low emission efficiency. When the temperature is above the LCST, the fluorescence emission increases rapidly. Eventually, the fluorescence intensity reaches a plateau (red arrow). This can be understood as follows. When the temperature is above the LCST, the fluorophores in the nanoparticles are exposed to a lack-of-water microenvironment in which the polarity is low, and thus the fluorophore has a high emission efficiency. Herein, the temperature range between the first local maximum and the LCST is the temperature transition bandwidth (*T*bw). If the *T*bw is as narrow as a few degrees, the Pluronic-fluorophore system can act as a switch, in which the fluorophore is considered to be at an "OFF" state when temperature is below the LCST, and at an "ON" state when temperature is above the first local maximum.

**Figure 5.** (**a**–**f**) Response of Pluronic F-127 loaded with fluorophores to the change in temperature. Black arrows show the LCST and red arrows the first local maximum (major slope change in the case of a plateau). (**g**) Pluronic F-127 nanoparticles loaded with fluorophore 4 acted as a very strong switch. Both F-127 nanoparticles loaded with fluorophore 2, 3 and 5, act as moderate switches. F-127 nanoparticles loaded with fluorophore 1, and 6 did not qualify as a switch due to having a large *T*BW (≥15 ◦C). (Values are based on Edge method).

For calculating the switching ratio (ION-to-IOFF), two methods are proposed: (1) The edge method, in which the amplitude of the voltage at the first local maximum (or in the case of a plateau, the first point at major slope change) is divided by the amplitude of the voltage at the LCST, and (2) the vicinity method, in which the amplitude of the voltage at the first local maximum is divided by the averaged amplitude of the voltage of the points at the vicinity of the LCST (the temperature of choice below LCST depends on the application, and 3 ◦C was chosen here).

As shown in Figure 5 and Table 4, Pluronic F-127 with fluorophore 4 had the highest ratio of switching among all other fluorophores (46.74 times). Since the TBW for this system is 6 ◦C, this polymer-fluorophore system qualified as a very strong switch.

Despite the (ION-to-IOFF) ratio of Pluronic F-127 with fluorophore 1 (8.37 times), this system doesn't behave as a switch. This is due to the very large *T*BW of more than 27 ◦C. Pluronic F-127 with fluorophores 2, 3 and 5 with an (ION-to-IOFF) of 4.97, 5.4 and 3.59 times, respectively, act as moderate switches. F-127 nanoparticle system with fluorophore 6, has an (ION-to-IOFF) of (1.81 times) over a wide bandwidth of 15 degrees and didn't behave as a switch.

Compared to F-127, Pluronic F-68 with fluorophore 4 elicits a higher value of ION-to-IOFF of 72.74 times (Figure 6). Based on the Pearson linear correlation coefficient, there was a strong inverse correlation between the switch ratio and the length of the hydrophobic chain of the Pluronic tri-block copolymer. As such, the shorter length of the hydrophobic chain of the Pluronic tri-block copolymer would yield a higher switching ratio (30 PPO units of F-68 compared to the 65 PPO units of F-127). Although future research is needed to delineate this phenomenon, we suspect that the fluorophores are embedded in the hydrophobic region of the micelles, and the shorter length of the hydrophobic chain would produce a less polar environment upon being heated.

**Table 4.** Summary of switching properties of the fluorophore-encapsulated Pluronic nanoparticles.

**\*** Lower critical solution temperature.

**Figure 6.** (**a**) Response of Pluronic F-127, F-98, and F-68 nanoparticles loaded with fluorophore 4 to the change in temperature. (**b**) Pluronic F-68 nanoparticles loaded with fluorophore 4 showed a stronger switching ratio compared to Pluronic F-127 nanoparticles (*p*-value of 0.009). The switch ratio is inversely correlated to length of hydrophobic (PPO) chain (ρ = −0.99, *p*-value = 0.018).

The observed significant polarity sensitivity of the fluorophores, considering their interconnected π-system, might be due to specific solvent-fluorophore interactions such as "charge transfer pathways," which mainly include photo-induced electron transfer (PeT) and internal charge transfer (ICT).