Nanosensores de Carbono e Machine Learning

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<p>Carbon nanosensors</p><p>and machine learning algorithms</p><p>for simultaneous measurement</p><p>of pH and temperature of aqueous media</p><p>Olga Sarmanova</p><p>Department of Physics</p><p>Lomonosov Moscow State University</p><p>Moscow, Russia</p><p>0000-0001-9797-2313</p><p>Sergey Burikov</p><p>Department of Physics</p><p>Lomonosov Moscow State University</p><p>Moscow, Russia</p><p>0000-0003-2271-8980</p><p>Kirill Laptinskiy</p><p>Skobeltsyn Institute of Nuclear Physics</p><p>Lomonosov Moscow State University</p><p>Moscow, Russia</p><p>0000-0003-2592-5942</p><p>Sergey Dolenko</p><p>Skobeltsyn Institute of Nuclear Physics</p><p>Lomonosov Moscow State University</p><p>Moscow, Russia</p><p>0000-0001-6214-3195</p><p>Maria Khmeleva</p><p>Skobeltsyn Institute of Nuclear Physics</p><p>Lomonosov Moscow State University</p><p>Moscow, Russia</p><p>0000-0003-1999-7552</p><p>Tatiana Dolenko</p><p>Department of Physics</p><p>Lomonosov Moscow State University</p><p>Moscow, Russia</p><p>0000-0003-2884-8241</p><p>Abstract—The research focuses on the creation of a</p><p>fluorescent carbon dots -based nanosensor that performs</p><p>simultaneous measurement of environmental temperature and</p><p>pH using machine learning algorithms. A comparative analysis</p><p>of multiple methods application - linear regression, the method</p><p>of partial least squares, artificial neural networks, random</p><p>forest, and gradient boosting - showed that multilayer</p><p>perceptron with 2 hidden layers containing 128 and 64 neurons</p><p>provided the lowest error in determining the temperature and</p><p>pH of the liquid medium containing carbon dots.</p><p>Keywords— pH and temperature nanosensor, carbon dots,</p><p>fluorescence, linear regression, partial least squares, random</p><p>forest, gradient boosting, artificial neural networks</p><p>I. INTRODUCTION</p><p>As a result of molecular biology's active development</p><p>and rapid progress in material science, approaches</p><p>established for the diseases' diagnostics and treatment at the</p><p>molecular level are becoming very promising [1]. To</p><p>implement these approaches, one needs to develop methods</p><p>of monitoring the organism condition at the cell level, in</p><p>vivo. It is known that cellular and intracellular processes</p><p>dramatically depend on the environmental pH value [2,3].</p><p>Thus, the 0.1 shift of the human blood pH outside the</p><p>reference values (7.35-7.45) leads to disruptions in the</p><p>cardiovascular system, abnormal intracellular pH value is</p><p>often a symptom of Alzheimer's disease, cancer, apoplexy</p><p>[4], and change of the pH value by 0.4 units can lead to death</p><p>[5]. Currently, methods of determining the pH values of</p><p>biotissues in vivo using nanoprobes are being actively</p><p>developed [6-8]. The authors [6] developed a fluorescent</p><p>sensor based on naphthalimide-rhodamine, based on the</p><p>resonant transfer of the fluorescence (FL) energy of different</p><p>fluorophores of the complex at different pH values. This</p><p>nanosensor provides pH detection in the range from 4.02 to</p><p>4.63 with an accuracy of 0.05. The synthesized complexes of</p><p>2-(6-(4-aminostyryl)-1,3-dioxo-1H-benzo[de]isoquinolin-</p><p>2(3H)-yl)-N, N-dimethylethanamine, which allow</p><p>determining the pH in the range from 3 to 6 units are also</p><p>based on the FL change induced by the shift of the medium's</p><p>pH value [7]. There are also other types of pH nanosensors.</p><p>For example, in the paper [8], it is proposed to measure pH</p><p>value of the environment using voltamperograms of</p><p>complexes obtained by physical sorption of chitosan on</p><p>hydroxylated quartz nanopipettes. The dynamic range of pH</p><p>values determined by such sensors is 2.6-10.7 with an</p><p>accuracy of 0.09 pH units.</p><p>Another important parameter that characterizes the state</p><p>of cells is temperature [9,10]. As known, temperature</p><p>changes indicate ongoing inflammatory processes in the</p><p>body [10], control of this parameter at the cellular level is</p><p>necessary during photothermal therapy [11,12]. Currently,</p><p>various methods of determining the tempera ture in v ivo are</p><p>proposed. In papers [13,14] for determination of the</p><p>temperature in cells, it is proposed to use FL of</p><p>nanodiamonds with NV or SiV centers. The authors [15,16]</p><p>developed nanothermosensors of the biological environment</p><p>based on complexes with ions of rare-earth elements. The</p><p>accuracy of temperature determination with these complexes</p><p>in the range of 24-44°C was 0.73°C. In [17], green</p><p>fluorescent proteins were introduced to measure the</p><p>temperature in cells, which enabled determining the</p><p>temperature with an accuracy of 0.4°C in the span from 20 to</p><p>60°C.</p><p>Even though cellular nanosensorics has been developing</p><p>rapidly in recent years, the problem of creating nanosensor</p><p>capable of simultaneous determination of several parameters</p><p>of biological environment is far from being solved. Carbon</p><p>dots (CD) with intense fluorescence can be used as such</p><p>nanosensors. Due to the sensitivity of this FL to changes of</p><p>ambient temperature [18,19], the types and number of ions</p><p>[20], to changes in pH value [21,22], as well as due to its</p><p>non-toxicity and biocompatibility, CD are worthy candidates</p><p>for the role of biomedical nanoagents. Several studies are</p><p>demonstrating the possibility of CD application to measure</p><p>temperature [23] and pH value [24] separately. However, we</p><p>could not find a publication on the creation of fluorescent</p><p>nanosensors based on CD, which ca n simultaneously</p><p>determine the pH value and ambient temperature. We</p><p>assume that solution of this two-parameter inverse problem</p><p>of optical spectroscopy requires using methods of machine</p><p>learning.</p><p>Russian Foundation for Basic Research, Foundation for the Advancement</p><p>of Theoretical Physics and Mathematics “BASIS”, The Development</p><p>program of the Interdisciplinary Scientific and Educational School of</p><p>Lomonosov Moscow State University «Photonic and Quantum</p><p>technologies. Digital medicine»</p><p>20</p><p>21</p><p>In</p><p>te</p><p>rn</p><p>at</p><p>io</p><p>na</p><p>l C</p><p>on</p><p>fe</p><p>re</p><p>nc</p><p>e</p><p>on</p><p>In</p><p>fo</p><p>rm</p><p>at</p><p>io</p><p>n</p><p>Te</p><p>ch</p><p>no</p><p>lo</p><p>gy</p><p>a</p><p>nd</p><p>N</p><p>an</p><p>ot</p><p>ec</p><p>hn</p><p>ol</p><p>og</p><p>y</p><p>(I</p><p>TN</p><p>T)</p><p>|</p><p>97</p><p>8-</p><p>1-</p><p>66</p><p>54</p><p>-3</p><p>21</p><p>7-</p><p>7/</p><p>21</p><p>/$</p><p>31</p><p>.0</p><p>0</p><p>©</p><p>20</p><p>21</p><p>IE</p><p>EE</p><p>|</p><p>D</p><p>O</p><p>I:</p><p>10</p><p>.1</p><p>10</p><p>9/</p><p>IT</p><p>N</p><p>T5</p><p>24</p><p>50</p><p>.2</p><p>02</p><p>1.</p><p>96</p><p>49</p><p>34</p><p>7</p><p>XXX-X-XXXX-XXXX-X/XX/$XX.00 ©20XX IEEEAuthorized licensed use limited to: UNIVERSIDADE FEDERAL DE SANTA CATARINA. Downloaded on September 25,2023 at 18:59:30 UTC from IEEE Xplore. Restrictions apply.</p><p>Fig. 1. Fluorescence spectra of CD aqueous suspensions, concentration</p><p>0.09 mg/mL at various temperature and pH values.</p><p>Over the past 10 years, artificial neural networks (ANN)</p><p>have been actively used to solve problems of medical</p><p>diagnostics. The authors [25, 26] used perceptrons to</p><p>recognize the alterations in the structure of proteins and</p><p>lip ids in in tumors by Raman spectra, which provided</p><p>diagnostics and determination of tumor size. The authors of</p><p>this publication have successfully used neural networks to</p><p>determine the presence and concentration of carbon</p><p>nanoagents in biological tissues using FL, absorption, and</p><p>Raman spectra [27-29]. The publication [30] presents the</p><p>results of solving multiparametric inverse problem of control</p><p>the removal of carbon nanocomposites (CD@PEG@folic</p><p>acid) and their components with urine by the FL spectra</p><p>using ANN.</p><p>There are rare studies where ANN were used for the</p><p>creation of fluorescent nanosensors. In [31], on the basis of</p><p>CdSe/ZnS quantum dots placed on the optical fiber surface,</p><p>fluorescent thermometers with the use of ANN were</p><p>developed. As a result of the experiment-based approach, the</p><p>root mean squared error of determining the temperature</p><p>using such nanosensors was 1.1 K, in the case of the quasi-</p><p>model approach - 0.29 K. In [32], the authors developed N-</p><p>doped carbon dots -based optical pH sensor. It operates in</p><p>broad range – from 2.0 to 14.0 pH units. Perceptron enabled</p><p>the determination of the hydrogen index value of the</p><p>nanoparticles' aqueous suspension with accuracy as low as</p><p>0.067 units, which was just 0.85% of the average pH value.</p><p>In this study we consider the application of a set of</p><p>machine learning methods to enable simultaneous</p><p>determination of pH and temperature</p><p>of the liquid medium</p><p>with the FL spectra of CD. The high sensitivity of</p><p>fluorescence of CD synthesized by the hydrothermal method</p><p>to changes of mentioned parameters of environment was</p><p>found. For solution of the two-parametric inverse problem of</p><p>determining the pH and temperature using CD FL spectra ,</p><p>the following methods were used: linear regression, the</p><p>method of partial least squares, artificial neural networks,</p><p>random forest, and gradient boosting.</p><p>II. MATERIALS AND METHODS</p><p>A. Research objects</p><p>CD were prepared from an aqueous-ammonia solution of</p><p>citric acid by the hydrothermal method described in detail in</p><p>[33] were used. CDs aqueous suspension were prepared</p><p>using deionized bidist illed water. Aqueous suspensions of</p><p>CD with concentration of nanoparticles 0.09 mg/mL were</p><p>prepared. The values of zeta potential and hydrodynamic</p><p>radius of the nanoparticles in the prepared suspension,</p><p>measured using Malvern ZetaSizer NanoZS (Malvern,</p><p>Worcestershire, UK), were (-30 mV) and 20 nm,</p><p>respectively.</p><p>B. Experimental section</p><p>We excited FL of CD in aqueous suspensions using diode</p><p>laser radiation with 405 nm wavelength and incident power</p><p>of 50 mW. The spectra were recorded using CCS-200</p><p>(Thorlabs) spectrometer. The practical spectral resolution of</p><p>the system was 2 nm.</p><p>We used Peltier elements placed at the opposite sides of</p><p>the cuvette to change the temperature of the sample. We</p><p>monitored the sample's temperature via a thermocouple</p><p>located in the suspension volume with 0.3oC precision.</p><p>III. RESULTS AND DISCUSSION</p><p>We investigated how pH and ambient temperature</p><p>influence the fluorescence spectra of CD. The pH values in</p><p>the CD aqueous suspensions varied in the range from 5 to</p><p>9.21. The pH was reduced by successive addition of aqueous</p><p>solution of HCl acid (Sigma Aldrich, 37%) with</p><p>concentration of 10 mM and pH=2 to the suspensions. The</p><p>pH was increased by adding aqueous solution of NaOH (Dia-</p><p>M) with concentration 10 mM and pH=12 to the suspension.</p><p>The temperature of the sample in the cuvette varied in the</p><p>range from 22oC to 81oC.</p><p>A. CD aqueous suspensions fluorescence depending on</p><p>pH and temperature values of the media</p><p>Fig.1 shows fluorescence spectra of CD aqueous</p><p>suspensions corresponding to various temperatures and pH</p><p>values of the suspensions. The narrow band in the region of</p><p>450-476 nm is conditioned by valence vibrations of water</p><p>OH-groups, the wide band in the region of 440-470 nm – by</p><p>CD fluorescence.</p><p>Fig.1 indicates that temperature and pH values of the</p><p>ambient media significantly affect the form of FL spectra of</p><p>CD aqueous suspensions. We used F0 parameter to quantify</p><p>the changes in FL spectra of CD aqueous suspensions. This</p><p>characteristic is equal to the ratio of the FL integral intensity</p><p>to the integral intensity of the Raman scattering band of OH-</p><p>groups valence vibrations (Fig.1).</p><p>Fig. 2 demonstrates the F0 parameter dependences on the</p><p>temperature at different pH values (Fig.2A) and the pH at</p><p>different temperature values (Fig.2B) in the studied ranges. It</p><p>follows that machine learning methods should be used to</p><p>solve the two-parameter inverse problem of fluorescence</p><p>spectroscopy to determine the pH and ambient temperature</p><p>with carbon dots simultaneously.</p><p>Authorized licensed use limited to: UNIVERSIDADE FEDERAL DE SANTA CATARINA. Downloaded on September 25,2023 at 18:59:30 UTC from IEEE Xplore. Restrictions apply.</p><p>Fig. 2. A) Temperature affecting F0 parameter of CD aqueous suspension at different pH values. B) Influence of pH value of the CD aqueous suspension</p><p>on the F0 parameter at different temperatures.</p><p>Fig. 3. A) Channel-by-channel statistics of the dataset. B) Dataset covariance matrix.</p><p>B. Experimental data preparation and description</p><p>The database obtained during the experiments on</p><p>fluorescence spectroscopy included 5600 fluorescence</p><p>spectra of CD aqueous suspensions corresponding to various</p><p>temperatures and pH of the suspension from the above-</p><p>mentioned ranges. The database included 15 series of the</p><p>samples FL spectra, each of them was obtained at the</p><p>following fixed pH values of suspensions: 5.53, 5.79, 6 .01,</p><p>6.19, 6.39, 6.57, 6.98, 7.21, 7.36, 7.52, 8.28, 8.58, 8.79, 9.00,</p><p>9.21. The suspensions temperature altered from 22 to 81oC</p><p>with an irregular step of hundredths degrees in each series.</p><p>Each spectra series at a fixed pH comprised from 234 to 462</p><p>examples.</p><p>At the stage of preprocessing the spectral data, we</p><p>channel-by-channel normalized the spectra by the area under</p><p>the band of OH-groups valence vibrations (460-476 nm) and</p><p>applied the Savitsky-Goley algorithm (first order polynomial,</p><p>window width of 51 channels equal to 11.4 nm) to smooth</p><p>the spectra .</p><p>Initially each sample (fluorescence spectra) contained</p><p>1473 input features – FL intensity values in spectral range</p><p>from 420 to 750 nm.</p><p>Note that the dimensionality of the input data radically</p><p>affects the quality of the inverse problem solution. As a rule,</p><p>each input feature contains a particular amount of</p><p>information. If you consider uninformative features, the</p><p>model may become too complex, which results in a decrease</p><p>in the inverse problem solution quality. Indeed, the spectral</p><p>distance between two adjacent channels is 0.22 nm</p><p>(considering that the spectral range of 330 nm comprises</p><p>1473 spectral channels). Nevertheless, the values of</p><p>fluorescence intensity in neighboring spectral channels are</p><p>highly correlated at the actual spectrometer resolution equal</p><p>to 2 nm. Therefore, their consideration won't increase the</p><p>information amount that the trained model obtains.</p><p>Consequently, a multiple reduction in the input features</p><p>number will simplify the model and decrease the error in</p><p>determining suspension’s pH and temperature.</p><p>Considering the above, we applied an aggregation to</p><p>reduce the input data dimensionality. It implied the creation</p><p>of a new input features set, where each feature equals the</p><p>mean value of fluorescence intensity in 10 adjacent spectral</p><p>channels from the original set. Thus, we reduced the input</p><p>data dimensionality from 1473 to 148.</p><p>Dataset statistics – mean (Mean), minimum (Min),</p><p>maximum (Max), values of fluorescence intensity, and its</p><p>Authorized licensed use limited to: UNIVERSIDADE FEDERAL DE SANTA CATARINA. Downloaded on September 25,2023 at 18:59:30 UTC from IEEE Xplore. Restrictions apply.</p><p>Fig. 4. A) Score plot for the first two PCs. B) Score plot for the first two PCs accounting for the temperature change within the pH series.</p><p>standard deviation (shaded area) in each spectral channel are</p><p>present in Fig.3A. From these data, it follows that input</p><p>features from a wide spectral range (460-650 nm) are</p><p>informative. Moreover, covariance matrix analysis (Fig.3B)</p><p>allows concluding, that features from this spectral range are</p><p>strongly correlated.</p><p>C. Principal component analysis</p><p>For preliminary data analysis, we used linear principal</p><p>component analysis (PCA) – a method of unsupervised linear</p><p>transformation of data [34], which has proven itself well in</p><p>various tasks, for example, for compressing hyperspectral</p><p>data [35]. PCA identifies orthogonal directions of the data</p><p>maximum variance (principal components) and projects the</p><p>data into a new subspace – the space of principal components</p><p>(PC), the dimensionality of which is determined by the</p><p>number of PC used.</p><p>The original dataset was compressed into a new two-</p><p>dimensional feature subspace. The data was scaled. We</p><p>found that it's enough to use the first two PCs to perform</p><p>preliminary data analysis as they provide 91.4% of the</p><p>explained variance. Figure 4A shows the score plot - original</p><p>spectra set projected into the space of the first two PC. The</p><p>closer any two points are located at the score plot, the more</p><p>similar the corresponding patterns (spectra) are. We</p><p>highlighted the values of the external parameters (pH and</p><p>temperature) with</p><p>colour to show the factors that affect</p><p>suspension's spectral characteristics most radically.</p><p>So, in Fig.4A, the examples corresponding to different</p><p>pH values are marked with different colors. Within the pH</p><p>series, the examples form a linear dependence, and its slope</p><p>differs from series to series. However, it does not seem</p><p>possible to identify an unambiguous relationship between the</p><p>slope and the pH value. There are areas on the score plot</p><p>where the examples belonging to different pH series lie close</p><p>to each other. This suggests that suspensions at significantly</p><p>different temperatures and pH values can have similar</p><p>fluorescence spectra. Such ambiguity and the nonlinear</p><p>dependence of the CD fluorescence characteristics on the pH</p><p>and temperature of the suspension suggest that linear</p><p>methods of data analysis will not solve this inverse problem</p><p>well. Th is is also evidenced by the score plot shown in Fig.</p><p>4B.</p><p>D. Machine learning algorithms application</p><p>To solve the problem of the ambient pH and temperature</p><p>determination with carbon dots from their FL spectra, a set of</p><p>machine learning methods was used: linear regression (LR)</p><p>in a linear basis with prior scaling of the data; partial least</p><p>squares (PLS) method; random forest (RF); gradient</p><p>boosting (GB) over decision trees; fully-connected neural</p><p>network – multilayer perceptron (MLP). We used Keras and</p><p>Scikit Learn Python libraries [36] to implement machine</p><p>learning models. In the framework of supervised learning,</p><p>adaptive algorithms of data analysis were trained on labeled</p><p>spectral data, i.e. each example (fluorescence spectrum of</p><p>CD suspension) from the database corresponded to the</p><p>temperature and pH values of the suspension, which had to</p><p>be determined in the result of the inverse problem solution.</p><p>There are several approaches to solve the N-parametric</p><p>inverse problem. In this paper, we use the sequential</p><p>definition approach. In this approach, first, N target</p><p>parameters are determined independently using N models</p><p>with one output each. Next, one selects the target parameter</p><p>that was determined best. To determine the next target</p><p>parameter one selects examples from the database</p><p>corresponding to the value of the first parameter defined</p><p>previously, and so on.</p><p>Thus, firstly, we used the full dataset (5600 examples) to</p><p>identify the pH value of the medium. Next, we selected</p><p>examples corresponding to the determined pH value and</p><p>formed a new dataset on their basis. We used the new</p><p>datased to determine the temperature of the suspension.</p><p>In this paper, the optimal parameters of the models were</p><p>selected to determine each target feature - pH and</p><p>temperature. The parameters used are shown in Table 1.</p><p>TABLE I. SETS OF OPTIMAL PARAMETERS OF THE MODELS</p><p>USED IN THE STUDY</p><p>Model</p><p>Model parameters</p><p>pH Temperature</p><p>Partial</p><p>Least</p><p>Squares</p><p>n_components = 10 n_components = 12</p><p>Random</p><p>Forest</p><p>max_depth = 3</p><p>max_features = 'sqrt'</p><p>n_estimators = 10</p><p>max_depth = 15</p><p>max_features = 1.0</p><p>n_estimators = 50</p><p>Gradient learning_rate = 0.2 learning_rate = 0.1</p><p>Authorized licensed use limited to: UNIVERSIDADE FEDERAL DE SANTA CATARINA. Downloaded on September 25,2023 at 18:59:30 UTC from IEEE Xplore. Restrictions apply.</p><p>Fig. 5. Results of the fluorescence spectroscopy inverse problem solution with machine learning algorithms.</p><p>Model</p><p>Model parameters</p><p>pH Temperature</p><p>Boosting max_depth = 3</p><p>max_features = 'sqrt'</p><p>n_estimators = 10</p><p>max_depth = 3</p><p>max_features = 'sqrt'</p><p>n_estimators = 100</p><p>Multilayer</p><p>Perceptron</p><p>• two hidden layers (128+64 neurons in them);</p><p>• logistic activation function in the hidden</p><p>layers and linear activation function in the output layer;</p><p>• optimizer = adam;</p><p>• loss = mean_absolute_error;</p><p>• stopping criterion: 500 epochs pass after</p><p>minimum error on the validation dataset;</p><p>• To avoid weights random initialization</p><p>affecting results, we trained each MLP 5 times and then</p><p>averaged the results of the training.</p><p>Using classical methods of data analysis (LR, PLS, RF,</p><p>GB), involved a random split of the dataset in the ratio of</p><p>80:20 into training and test sets. As for the MLP, we sp lit the</p><p>dataset into training, validation, and test sets in a ratio of</p><p>70:20:10. The authors used 5-fold cross-validation to prevent</p><p>the data splitting affect the result.</p><p>E. Comparative analysis of the machine learning</p><p>algorithms effectiveness</p><p>Fig.5 shows the mean absolute error of determining</p><p>temperature and pH values reached at the test set with the</p><p>studied machine learning algorithms.</p><p>The obtained results allow concluding that the linear</p><p>models (LR and PLS) performed the worst in solving the</p><p>inverse problem. This was to be expected, since already at</p><p>the stage of applying PCA (p. 3. 3.), complex and nonlinear</p><p>dependence of target parameters on input features was</p><p>demonstrated (Fig. 2) – the ambiguity of the spectrum shape</p><p>dependence on a pair of values (pH, temperature). The LR</p><p>method allows determining the pH and temperature with</p><p>mean absolute error (MAE) of 0.27 pH units and 8.2oC,</p><p>respectively.</p><p>Nonlinear machine learning methods have demonstrated</p><p>the best results in so lving the problem. These algorithms</p><p>showed almost identical results, taking into account the error</p><p>introduced by the way the data was split into sets. The</p><p>minimum MAE of pH and temperature determination was</p><p>obtained using MLPs and was 0.005 ± 0.001 pH units and</p><p>0.67±0.07oC, respectively.</p><p>IV. CONCLUSION</p><p>In the study, we researched machine learning algorithms'</p><p>application to create a fluorescent carbon nanosensor for the</p><p>simultaneous measurement of ambient temperature and pH.</p><p>The study shows the FL of CD synthesized via hydrothermal</p><p>route to be highly sensitive to changes in pH and ambient</p><p>temperature. To solve the multiparameter inverse problem of</p><p>optical spectroscopy, a number of machine learning methods</p><p>were used: linear regression, the method of partial least</p><p>squares, artificial neural networks, random forest, and</p><p>gradient boosting. A comparative analysis of the</p><p>effectiveness of these methods showed tha t multilayer</p><p>perceptron provide the smallest mean absolute error of</p><p>ambient temperature and pH determination with carbon dots.</p><p>In the study, we demonstrated a method that enables</p><p>simultaneous measurement of environmental temperature</p><p>and pH with mean absolute error as low as 0.67oC and 0.005,</p><p>respectively. We obtained this result with multilayer</p><p>perception on the database comprising f luorescence spectra</p><p>of CD aqueous suspensions with reduced input features</p><p>dimensionality. The obtained results highlight wide</p><p>opportunities for the development of an optical multimodal</p><p>nanosensor based on CD.</p><p>ACKNOWLEDGMENT</p><p>The contribution of O.E. Sarmanova (application of</p><p>machine learning algorithms) was supported by the</p><p>Foundation for the Advancement of Theoretical Physics and</p><p>Mathematics “BASIS” (Project No. 19-2-6-6-1). The</p><p>research has been supported by Russian Foundation for Basic</p><p>Research (project No. 20-32-70150). This study was</p><p>performed according to the Development program of the</p><p>Interdisciplinary Scientific and Educationa l School of</p><p>Lomonosov Moscow State University «Photonic and</p><p>Quantum technologies. Digital medicine» (T.A.Dolenko).</p><p>The authors express their deep gratitude to A. E. Tomskaya</p><p>for the carbon dots synthesis.</p><p>REFERENCES</p><p>[1] S. Shrivastava, S. Jain, D. Kumar, S. Soni, and M.S. 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Restrictions apply.</p><p>presenting extreme acidosis (pH</p>