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Diabetic neuropathy (DN) is one of the prevalent forms of neuropathy that involves alterations in biomechanical changes in the human gait. Diabetic foot ulceration (DFU) is one of the pervasive types of complications that arise due to DN. In the literature, for the last 50 years, researchers have been trying to observe the biomechanical changes due to DN and DFU by studying muscle electromyography (EMG) and ground reaction forces (GRF). However, the literature is contradictory. In such a scenario, we propose using Machine learning techniques to identify DN and DFU patients by using EMG and GRF data. We collected a dataset from the literature which involves three patient groups: Control (n = 6), DN (n = 6), and previous history of DFU (n = 9) and collected three lower limb muscles EMG (tibialis anterior (TA), vastus lateralis (VL), gastrocnemius lateralis (GL)), and three GRF components (GRFx, GRFy, and GRFz). Raw EMG and GRF signals were preprocessed, and different feature extraction techniques were applied to extract the best features from the signals. The extracted feature list was ranked using four different feature ranking techniques, and highly correlated features were removed. In this study, we considered different combinations of muscles and GRF components to find the best performing feature list for the identification of DN and DFU. We trained eight different conventional ML models: Discriminant analysis classifier (DAC), Ensemble classification model (ECM), Kernel classification model (KCM), k-nearest neighbor model (KNN), Linear classification model (LCM), Naive Bayes classifier (NBC), Support vector machine classifier (SVM), and Binary decision classification tree (BDC), to find the best-performing algorithm and optimized that model. We trained the optimized the ML algorithm for different combinations of muscles and GRF component features, and the performance matrix was evaluated. Our study found the KNN algorithm performed well in identifying DN and DFU, and we optimized it before training. We found the best accuracy of 96.18% for EMG analysis using the top 22 features from the chi-square feature ranking technique for features from GL and VL muscles combined. In the GRF analysis, the model showed 98.68% accuracy using the top 7 features from the Feature selection using neighborhood component analysis for the feature combinations from the GRFx-GRFz signal. In conclusion, our study has shown a potential solution for ML application in DN and DFU patient identification using EMG and GRF parameters. With careful signal preprocessing with strategic feature extraction from the biomechanical parameters, optimization of the ML model can provide a potential solution in the diagnosis and stratification of DN and DFU patients from the EMG and GRF signals.

In this paper, we studied the possibility of increasing the Brillouin frequency shift (BFS) detection accuracy in distributed fibre-optic sensors by the separate and joint use of different algorithms for finding the spectral maximum: Lorentzian curve fitting (LCF, including the Levenberg–Marquardt (LM) method), the backward correlation technique (BWC) and a machine learning algorithm, the generalized linear model (GLM). The study was carried out on real spectra subjected to the subsequent addition of extreme digital noise. The precision and accuracy of the LM and BWC methods were studied by varying the signal-to-noise ratios (SNRs) and by incorporating the GLM method into the processing steps. It was found that the use of methods in sequence gives a gain in the accuracy of determining the sensor temperature from tenths to several degrees Celsius (or MHz in BFS scale), which is manifested for signal-to-noise ratios within 0 to 20 dB. We have found out that the double processing (BWC + GLM) is more effective for positive SNR values (in dB): it gives a gain in BFS measurement precision near 0.4 °C (428 kHz or 9.3 με); for BWC + GLM, the difference of precisions between single and double processing for SNRs below 2.6 dB is about 1.5 °C (1.6 MHz or 35 με). In this case, double processing is more effective for all SNRs. The described technique’s potential application in structural health monitoring (SHM) of concrete objects and different areas in metrology and sensing were also discussed.

Life was once normal before the first announcement of COVID-19’s first case in Wuhan, China, and what was slowly spreading became an overnight worldwide pandemic. Ever since the virus spread at the end of 2019, it has been morphing and rapidly adapting to human nature changes which cause difficult conundrums in the efforts of fighting it. Thus, researchers were steered to investigate the virus in order to contain the outbreak considering its novelty and there being no known cure. In contribution to that, this paper extensively reviewed, compared, and analyzed two main points; SARS-CoV-2 virus transmission in humans and detection methods of COVID-19 in the human body. SARS-CoV-2 human exchange transmission methods reviewed four modes of transmission which are Respiratory Transmission, Fecal–Oral Transmission, Ocular transmission, and Vertical Transmission. The latter point particularly sheds light on the latest discoveries and advancements in the aim of COVID-19 diagnosis and detection of SARS-CoV-2 virus associated with this disease in the human body. The methods in this review paper were classified into two categories which are RNA-based detection including RT-PCR, LAMP, CRISPR, and NGS and secondly, biosensors detection including, electrochemical biosensors, electronic biosensors, piezoelectric biosensors, and optical biosensors.

Chronic kidney disease (CKD) is one of the severe side effects of type 1 diabetes mellitus (T1DM). However, the detection and diagnosis of CKD are often delayed because of its asymptomatic nature. In addition, patients often tend to bypass the traditional urine protein (urinary albumin)-based CKD detection test. Even though disease detection using machine learning (ML) is a well-established field of study, it is rarely used to diagnose CKD in T1DM patients. This research aimed to employ and evaluate several ML algorithms to develop models to quickly predict CKD in patients with T1DM using easily available routine checkup data. This study analyzed 16 years of data of 1375 T1DM patients, obtained from the Epidemiology of Diabetes Interventions and Complications (EDIC) clinical trials directed by the National Institute of Diabetes, Digestive, and Kidney Diseases, USA. Three data imputation techniques (RF, KNN, and MICE) and the SMOTETomek resampling technique were used to preprocess the primary dataset. Ten ML algorithms including logistic regression (LR), k-nearest neighbor (KNN), Gaussian naïve Bayes (GNB), support vector machine (SVM), stochastic gradient descent (SGD), decision tree (DT), gradient boosting (GB), random forest (RF), extreme gradient boosting (XGB), and light gradient-boosted machine (LightGBM) were applied to developed prediction models. Each model included 19 demographic, medical history, behavioral, and biochemical features, and every feature’s effect was ranked using three feature ranking techniques (XGB, RF, and Extra Tree). Lastly, each model’s ROC, sensitivity (recall), specificity, accuracy, precision, and F-1 score were estimated to find the best-performing model. The RF classifier model exhibited the best performance with 0.96 (±0.01) accuracy, 0.98 (±0.01) sensitivity, and 0.93 (±0.02) specificity. LightGBM performed second best and was quite close to RF with 0.95 (±0.06) accuracy. In addition to these two models, KNN, SVM, DT, GB, and XGB models also achieved more than 90% accuracy.

The propagation of viruses has become a global threat as proven through the coronavirus disease (COVID-19) pandemic. Therefore, the quick detection of viral diseases and infections could be necessary. This study aims to develop a framework for virus diagnoses based on integrating photonics technology with artificial intelligence to enhance healthcare in public areas, marketplaces, hospitals, and airfields due to the distinct spectral signatures from lasers’ effectiveness in the classification and monitoring of viruses. However, providing insights into the technical aspect also helps researchers identify the possibilities and difficulties in this field. The contents of this study were collected from six authoritative databases: Web of Science, IEEE Xplore, Science Direct, Scopus, PubMed Central, and Google Scholar. This review includes an analysis and summary of laser techniques to diagnose COVID-19 such as fluorescence methods, surface-enhanced Raman scattering, surface plasmon resonance, and integration of Raman scattering with SPR techniques. Finally, we select the best strategies that could potentially be the most effective methods of reducing epidemic spreading and improving healthcare in the environment.

The incorporation of a proper sensing material towards the construction of high selectivity optical sensing devices is vital. Polysaccharides, such as chitosan and carrageenan, are among the bio-based sensing materials that are extensively employed due to their remarkable physicochemical attributes. This paper highlights the critical aspects of the design of suitable polysaccharides for the recognition of specific analytes through physical and chemical modifications of polysaccharide structure. Such modifications lead to the enhancement of physicochemical properties of polysaccharides and optical sensor performance. Chitosan and carrageenan are two materials that possess excellent features which are capable of sensing target analytes via various interactions. The interaction between polysaccharides and analytes is dependent on the availability of functional groups in their structure. The integration of polysaccharides with various optical sensing techniques further improves optical sensor performance. The application of polysaccharides as sensing materials in various optical sensing techniques is also highlighted, particularly for metal ion sensing.

Optical imaging is crucial for cell examination, but its instruments' bulky optics limit their flexibility, and a single wavelength provides limited sample information. This paper presents a fibre cantilever-based endoscopic scanner for multiwavelength reflectance confocal microscopy. The scanner utilises the dual-core fibre cantilever to produce Lissajous scans while accommodating two distinct wavelengths. Coloured surfaces were imaged at wavelengths of 520 nm, 635 nm and 850 nm, and verified with a colour chart and reflectance spectroscopy. Blue ink-stained plant stem cross-section samples were also imaged and compared. Furthermore, the scanner performed structure morphology imaging on cellular samples. The 520 nm and 635 nm wavelengths create low- and high-contrast images respectively, while the 850 nm wavelength delivers a sharper image while also capturing the surface profile of the target sample. The findings demonstrated the endoscopic scanner’s capability to extract information based on different wavelengths, providing a more diverse view of the sample and enhancing the surface profile. Using 635 nm and 850 nm, the reconstructed images have a contrast enhancement of 67.9% and 90.8% respectively from 520 nm. The smallest feature size measured on the cellular sample is 5 µm. The endoscopic scanner has the potential to study microfluidic and cell morphology.

AbstractCarbon dots (CDs)-based fluorescence imaging with confocal reflectance effectively localises labelled structures with clear sample visualization. Yet, integrating these modalities into single-fibre cantilever-based endoscopic scanners poses challenges. Thus, we present an endoscopic scanner with a mixed-mode fibre cantilever for dual modalities imaging. The cantilever features two-attached fibre construction for multiple illumination and detection channels without additional optical components. The measured lateral and axial resolutions are 5.81 and 6.52 µm, respectively with field-of-view of 408 × 488 µm. The fluorescence detection capability was first verified using fluorescent microspheres and CD-embedded polymer film, followed by confocal reflectance and CDs-based fluorescence imaging on labelled fibrous network and plant samples. The results demonstrate the complementary strengths of confocal reflectance, which extracts surface profiles, and CDs-based fluorescence, which captures fluorescence signals, leading to precise localization of fluorescence signals within the sample. Switching modality schemes at the scanner’s back-end enables implementation of different modalities.

Physiological signal measurement and processing are increasingly becoming popular in the ambulatory setting as the hospital-centric treatment is moving towards wearable and ubiquitous monitoring. Most of the physiological signals are highly susceptible to various types of noises, especially movement artifacts. The electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) signals are no exception to motion artifacts, which become prominent in the ambulatory setting. Since successful detection of various neurological disorders is greatly dependent upon clean EEG and fNIRS signals, it is a matter of utmost importance to remove motion artifacts from these two signal modalities using reliable and robust methods. This paper proposes three novel multiresolution analysis techniques: i) Variational mode decomposition (VMD), ii) VMD in combination with principal component analysis (VMD-PCA), and iii) VMD in combination with canonical correlation analysis (VMD-CCA), for motion artifact correction from single-channel EEG and fNIRS signals. The efficacy of these novel techniques is validated by computing the difference in the signal to noise ratio ( $\Delta SNR$ ) and percentage reduction in motion artifacts ( $\eta$ ). Among the three proposed novel methods, VMD-CCA decomposed with 15 intrinsic mode functions (IMFs) has shown the best denoising performance for EEG signals producing an average $\Delta SNR$ and $\eta $ values of 23.81 dB and 57.01%, respectively for all 23 EEG recordings. On the other hand, for the available 16 fNIRS recordings, VMD-CCA decomposed with 10 IMFs produced an average $\Delta SNR$ and $\eta $ values of 15.97 dB and 39.01%, respectively. The results reported using the proposed methods outperform most of the existing state-of-the-art techniques.

Abstract The resistive switching (RS) mechanism is resulted from the formation and dissolution of a conductive filament due to the electrochemical redox-reactions and can be identified with a pinched hysteresis loop on the I–V characteristic curve. In this work, the RS behaviour was demonstrated using a screen-printed electrode (SPE) and was utilized for creatinine sensing application. The working electrode (WE) of the SPE has been modified with a novel small organic molecule, 1,4-bis[2-(5-thiophene-2-yl)-1-benzothiopene]-2,5-dioctyloxybenzene (BOBzBT2). Its stability at room temperature and the presence of thiophene monomers were exploited to facilitate the cation transport and thus, affecting the high resistive state (HRS) and low resistive state (LRS) of the electrochemical cell. The sensor works based on the interference imposed by the interaction between the creatinine molecule and the radical cation of BOBzBT2 to the conductive filament during the Cyclic Voltammetry (CV) measurement. Different concentrations of BOBzBT2 dilution were evaluated using various concentrations of non-clinical creatinine samples to identify the optimised setup of the sensor. Enhanced sensitivity of the sensor was observed at a high concentration of BOBzBT2 over creatinine concentration between 0.4 and 1.6 mg dL−1—corresponding to the normal range of a healthy individual.

In the era of smart Internet-of-Things, convolutional neural network (CNN) models with low computational overhead are crucial for low-latency applications in resource-constrained embedded devices. The performance and efficiency of multiplication operations play a vital role in accelerating and optimizing CNN computation. In this article, we propose the MA4C technique, which reduces CNN computation overhead by converting a pretrained CNN’s parameters into approximated minimum signed digit (MSD) representations. MSD representation contains fewer non-zero digits on average compared to the binary representation of a number. The proposed scheme approximates the MSD representations by only considering a specified number of most significant digits. The MA4C technique reduces the computational complexity of multipliers by reducing the number of partial sums. The proposed MSD approximation was applied on various DNN models, and their performance was analyzed for different datasets, varying CNN depth, and network configuration. Implementation of our proposed method on FPGA reduced the logic circuits and multiplier latency by up to 4.2× and 1.2× respectively compared to an 8-bit Booth multiplier for most CNN models.

An evaluation of a novel spectrum features combining the distant longer and shorter wavelengths of a single chirped fiber Bragg grating (CFBG) for temperature and transverse forces discrimination is experimentally demonstrated. The shift of the two distant wavelengths’ pair is compared with the conventional pair combining the bandwidth modulation and center wavelength shift. The CFBG sensor is simply bonded to a cantilever beam and subjected to transverse loading (four times) and a heating-cooling cycle. The transverse forces calibration results show a repeatability of 3.9 pm and 1.7 pm for the bandwidth’s and center wavelength’s responses, respectively, while the distant wavelengths’ show a repeatability of 2.37 pm and 3.01 pm, respectively. The cantilever CFBG sensor exhibits high correlation coefficients of 0.9 between the two heating and cooling data sets, except for the bandwidth, which only had a lower coefficient of 0.75. The linear model of both pairs for calculating temperature and transverse forces can provide an accurate estimate, with the longer-shorter wavelengths’ pair having an advantage over the pair of the bandwidth-centre-wavelength. The study has demonstrated the feasibility of the method proposed by our group in a previous work, by which three physical quantities can be measured with a single custom FBG.

Miniaturized endoscopic scanners have had a significant impact on high-resolution optical imaging. Technological advancements in micro-electromechanical systems and optical fiber technology have resulted in various optical-mechanical configurations designed to fulfill specific requirements. However, it is still challenging to provide comprehensive, undistorted images with high-resolution images of target samples. This paper reviews the optical imaging techniques utilized in cantilever-based endoscopic scanners by analyzing and comparing their key performances, pros and cons, and corresponding optical components needed to develop the system. The concept of multimodal imaging is then highlighted by discussing its principle and current status in endoscopic scanners. We also reviewed the scanning configurations concerning their mechanical components, general structures, and drive signals for different scanning patterns. The feedback control aspect in endoscopic scanners is then highlighted. We discuss its role in mitigating undesired nonlinear vibration effects and provide a survey of the current implementations. Finally, we discuss the endoscopic scanners’ current and potential applications, artificial intelligence techniques in image reconstruction, and disease detection and provide recommendations on endoscopic scanner system design for future reference.

Respiratory ailments are a very serious health issue and can be life-threatening, especially for patients with COVID. Respiration rate (RR) is a very important vital health indicator for patients. Any abnormality in this metric indicates a deterioration in health. Hence, continuous monitoring of RR can act as an early indicator. Despite that, RR monitoring equipment is generally provided only to intensive care unit (ICU) patients. Recent studies have established the feasibility of using photoplethysmogram (PPG) signals to estimate RR. This paper proposes a deep-learning-based end-to-end solution for estimating RR directly from the PPG signal. The system was evaluated on two popular public datasets: VORTAL and BIDMC. A lightweight model, ConvMixer, outperformed all of the other deep neural networks. The model provided a root mean squared error (RMSE), mean absolute error (MAE), and correlation coefficient (R) of 1.75 breaths per minute (bpm), 1.27 bpm, and 0.92, respectively, for VORTAL, while these metrics were 1.20 bpm, 0.77 bpm, and 0.92, respectively, for BIDMC. The authors also showed how fine-tuning a small subset could increase the performance of the model in the case of an out-of-distribution dataset. In the fine-tuning experiments, the models produced an average R of 0.81. Hence, this lightweight model can be deployed to mobile devices for real-time monitoring of patients.

An intelligent insole system may monitor the individual’s foot pressure and temperature in real-time from the comfort of their home, which can help capture foot problems in their earliest stages. Constant monitoring for foot complications is essential to avoid potentially devastating outcomes from common diseases such as diabetes mellitus. Inspired by those goals, the authors of this work propose a full design for a wearable insole that can detect both plantar pressure and temperature using off-the-shelf sensors. The design provides details of specific temperature and pressure sensors, circuit configuration for characterizing the sensors, and design considerations for creating a small system with suitable electronics. The procedure also details how, using a low-power communication protocol, data about the individuals’ foot pressure and temperatures may be sent wirelessly to a centralized device for storage. This research may aid in the creation of an affordable, practical, and portable foot monitoring system for patients. The solution can be used for continuous, at-home monitoring of foot problems through pressure patterns and temperature differences between the two feet. The generated maps can be used for early detection of diabetic foot complication with the help of artificial intelligence.

A label-free detection for dissolved carbon dioxide (dCO2) is developed using a tapered optical fiber sensor. The tapered region of the optical fiber is coated with the zinc oxide (ZnO) nanorice and used as a probe for dCO2 sensing. The sensor probe was exposed to different concentrations of dCO2 solution ranging from 10 to 100 ppm. ZnO nanorice can adsorb dCO2 via strong hydrogen bonding due to the presence of plenty of oxygen atoms on its surface layer. The interaction between ZnO nanorice and dCO2 changes the optical properties of the ZnO nanorice layer, resulting in the change in reflectance. From the experiment, the result shows that there is an improvement in the sensitivity of the sensor when higher concentration was used. A broad linear trend ranging from 0 to 60 ppm ( $R^{2} = 0.972$ ) is observed for the sensor probe that is coated with 1.0 M of ZnO nanorice compared with the 0.1 M and 0.5 M ZnO nanorice concentrations. The sensor sensitivity obtained is 0.008 mW/ppm. The sensor demonstrates a response and recovery time of 0.47 and 1.70 min, respectively. Good repeatability is obtained with the standard deviation in the range of 0.008–0.027. The average resolution calculated for this sensor is 4.595 ppm.

A novel bio-based polycationic polyurethane as an ion-selective membrane for nitrate sensing was successfully developed. In this work, the intermolecular interactions at active polymeric sites play a primary role in selective nitrate-ion detection. From the experiment, FTIR shows a significant shift from 1543 cm-1 to 1548 cm-1 in N-H bending, indicating that intermolecular interactions occur between the polycationic polyurethane and nitrate. AFM shows that the surface roughness of the polycationic polyurethane decreases from 95.7 nm to 12.2 nm after immersion in nitrate solution. Meanwhile, FESEM images show that the bright area, which represents the hard segment of polycationic polyurethane, decreases after immersion, indicating that the nitrate is interacting with the hard segment of the polycationic polyurethane via intermolecular interaction. Furthermore, EIS shows that the conductivity increases from 2.84 x 10-11 to 5.34 x 10-11 S cm-1 after ion exchange occurs between the iodide and nitrate on the polycationic polyurethane. To assess the sensing performance, the sensor probe is fabricated by coating the polycationic polyurethane thin film on the tapered region of an optical fiber. Rapid detection, good repeatability, and a sensitivity of 5.94 x 10-2μW/ppm are obtained for nitrate detection using the above bio-based-sensing material. The selectivity study also shows that the sensing material possesses high affinity toward the nitrate ion.

We report on the experimental results of the combination of unique auto-correlation properties of Golay complementary code and Hadamard matrix properties of standard simplex code. The combination of the two coding techniques results in improvement of the signal-to-noise ratio (SNR) of time domain multiplexed fiber Bragg grating (TDM-FBG) sensor for temperature measurement. Previously, we have analyzed the properties of both coding techniques when deployed separately in the TDM-FBG sensor. In this case, both coding techniques result in the same amount of SNR improvement for code length longer than 31 bits. In this paper, we demonstrate the combination and simultaneous deployment of the two techniques to measure multiple FBGs under room condition (25 °C) and 50 °C temperature. The two schemes combination results in remarkable improvement of SNR and still retains the original spatial property of the decoded FBG signals, confirming the successful deployment of the hybrid codes. From the measurement of two FBG sensors located after 16 km of fiber, the combination of 31 bits of simplex- and 64 bits of Golay codes has resulted in a total of 10.5 dB improvement of SNR.

Decoupling between axial and transversal forces is an essential during tool-tissue interaction in many medicine surgeries; in particular where fine and precise manipulation is required to save the delicate tissues. One example of which is the vitreoretinal microsurgery (VRMS). When fiber Bragg grating based sensing technique is utilized, the cross-talk noise between the axial and transversal forces always show up severely due to its conventional wavelength shift method. To address the challenge, we have introduced theoretical method in which a combination of tapered FBG (TFBG) and bandwidth modulation method instead are used. Here in this paper, first phase of our ongoing progress to proof the concept and validate the simulation results is demonstrated experimentally. We have developed a prototype incorporated TFBG mimicking the structure of the ophthalmia's needle to measure temperature-insensitive 1-DOF axial forces. High speed plug and play (I-MON-256USB (Ibsen)) is used to monitor the reflection spectrum of the prototype sensor. An automated calibration system using LabVIEW with efficient algorithms have been developed to calculate and keep track the bandwidth variations as different values of axial forces applied. Calibration procedures are repeated three times to validate the consistency of the sensor response. Experimental results show that, the estimated force values of our prototype are consistent with their actual values with RMS error less than 0.356 N over the range (1N-10N), while temperature insensitivity is guaranteed.

Polyurethane-graphene (PU-graphene) nanocomposite was utilized as the sensing material for a fiber Bragg grating (FBG) temperature sensor. The nanocomposite was characterized using a Fourier transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), scanning electron microscopy (SEM), and electrochemical impedance spectroscopy (EIS) to study the morphology and physical properties of the materials for FBG temperature sensing application. The physical, chemical, and conductivity of PU-graphene improve after graphene was introduced in pristine PU. The FTIR shows that the strong intermolecular interaction between -O-C = O (ester) and hydrogen in graphene in the PU-graphene was indicated by the shift to lower wavenumber of ether (C-O-C) peak at -1220 cm-1 to -1218 cm-1. TGA shows the thermal stability of PU increases to 217 °C due to the strong intermolecular interaction with the presence of graphene flakes. EIS shows a good electrical conductivity of 1.39 × 10-9 Scm-1 in the PU-graphene due to the electron transfer provided by the graphene. The SEM shows a rough and uneven texture on the surface of FBG coated by PU-graphene nanocomposite which shows that the graphene flakes are completely coated by polyurethane polymer. The PU-graphene was then dip-coated on the optical fiberbased Bragg grating, and the sensor performance for a temperature sensor was evaluated, where a good linearity with the sensitivity of 6 pm/°C was achieved.

This paper experimentally demonstrates the feasibility of improving the sensing performance of fiber Bragg grating (FBG)-based point sensors by deploying two pulse coding techniques, simplex coding (SSC), and Golay complementary codes (GCC). The two techniques are separately combined with the conventional time-domain multiplexed FBG (TDM-FBG) interrogation sensor, and their results are compared with respect to the conventional single pulse interrogation technique. Consistently with the theory, for both the coding techniques, we have confirmed that the signal-to-noise ratio improved proportionally to the code length. In details, for both the techniques, the signal-to-noise improvement ratio of two FBGs located after around 16.35 km of fiber has significantly increased up to about 6 dB. Furthermore, the decoded signals from both techniques conserve their original spatial properties, confirming the successfulness of Hadamard transform and Golay auto-correlation calculations. Our experimental analysis confirms the simplex codes with their significant noise reduction ability, which is translated into well formed and accurate mean amplitude calculations. Moreover, the analysis of TDM-FBG-based GCCs have confirmed the unique properties of the auto-correlation process to increase the FBG signals up to around 128 times compared with the single pulse case.

This paper demonstrates carbon quantum dots (CQDs) with triangular silver nanoparticles (AgNPs) as the sensing materials of localized surface plasmon resonance (LSPR) sensors for chlorophyll detection. The CQDs and AgNPs were prepared by a one-step hydrothermal process and a direct chemical reduction process, respectively. FTIR analysis shows that a CQD consists of NH2, OH, and COOH functional groups. The appearance of C=O and NH2 at 399.5 eV and 529.6 eV in XPS analysis indicates that functional groups are available for adsorption sites for chlorophyll interaction. A AgNP–CQD composite was coated on the glass slide surface using (3-aminopropyl) triethoxysilane (APTES) as a coupling agent and acted as the active sensing layer for chlorophyll detection. In LSPR sensing, the linear response detection for AgNP–CQD demonstrates R2 = 0.9581 and a sensitivity of 0.80 nm ppm−1, with a detection limit of 4.71 ppm ranging from 0.2 to 10.0 ppm. Meanwhile, a AgNP shows a linear response of R2 = 0.1541 and a sensitivity of 0.25 nm ppm−1, with the detection limit of 52.76 ppm upon exposure to chlorophyll. Based on these results, the AgNP–CQD composite shows a better linearity response and a higher sensitivity than bare AgNPs when exposed to chlorophyll, highlighting the potential of AgNP–CQD as a sensing material in this study.