Machine learning-enhanced fluorescence signal processing of carbon quantum dots for high-accuracy chemical sensing

Advances in Machine Learning-Assisted Optical Sensing Arrays for Disease Diagnosis

This review provides a systematic overview of ML-reinforced optical sensing arrays, with a particular focus on three major modalities: colorimetric, fluorescent, and surface-enhanced Raman scattering (SERS) sensor arrays.

Contribution of AI and Machine Learning in the Optimization of Ultrasensitive Biosensors: Review

Artificial intelligence (AI) and machine learning (ML) are reshaping the design, fabrication, and deployment of ultrasensitive biosensors by enabling data-driven optimization across the entire sensing pipeline. This review examines how ML assisted strategies enhance analytical performance, including limit of detection, selectivity, dynamic range, stability, and response time, in electrochemical, optical (surface plasmon resonance, surface-enhanced Raman spectroscopy, fluorescence), piezoelectric, and field-effect transistor-based biosensors. The principal ML examples used in biosensing are summarized: supervised learning for calibration and multi-analyte quantification, unsupervised learning for drift correction and background separation, and deep learning for feature extraction from high-dimensional spectra, impedance signatures, and microfluidic images. Special attention is given to AI-enabled signal processing methods that suppress noise, compensate for temperature and matrix effects, and improve robustness in complex biological samples. Moreover, AI applications in sensor optimization are discussed, including Bayesian optimization, active learning, physics-informed machine learning, and digital twin models. The role of AI in intelligent point-of-care platforms, edge deployment, smartphone readouts, and federated learning is also examined. Persistent challenges, including limited labeled data, dataset shift, lack of reporting standards, model interpretability, and regulatory validation, are analyzed alongside recommended best practices such as uncertainty quantification, explainable AI, and cross-laboratory validation.

Machine learning-enhanced temperature sensing of spectrally featureless fluorophores.

Current efforts in fluorescence-based optical thermometry have concentrated on probe optimization to minimize prediction errors, typically requiring fluorophores with multiple emission bands for ratiometric analysis or large emission peak shifts for reliable calibration. However, fluorophores exhibiting a single emission band with a modest peak shift - here termed spectrally featureless - are excluded from conventional thermometric approaches, as single-band intensity measurements are susceptible to excitation source fluctuations and photobleaching, while their peak shifts alone are insufficient for minimizing prediction errors in conventional temperature estimation methods based on calibration curves. Integration of machine learning (ML) with spectral analysis offers a promising route to overcome these limitations, yet has not been widely explored in this field. In this study, we aimed to optimize ML algorithms for fluorescence-based temperature sensing using resveratrone - a spectrally featureless organic fluorophore exhibiting a single emission band and a modest peak shift under temperature variation - as the sensing probe. We tested four different ML models: multiple linear regression, support vector regression, linear random forest, and multilayer perceptron algorithms as the primary regression frameworks, alongside conventional chemometric comparators for comparison. Our comparison showed that the pyramid-structure multilayer perceptron worked best at capturing temperature-dependent spectral changes and outperformed the other models in testing, achieving a relative performance index of 170 (with the single-feature polynomial regression baseline defined as 100), reflecting the lowest test MAE among all models evaluated. This advantage persisted under an independently measured condition involving a different day, concentration, and solvent matrix, where the pyramid-structure multilayer perceptron outperformed the other models by more than 1.2 °C in test MAE. This approach may help expand the range of viable materials for temperature sensing by enabling accurate temperature predictions from steady-state photoluminescence spectra of resveratrone, supporting potential applications of fluorescence nanothermometry within the temperature range investigated in this study (25-70 °C), which encompasses biologically and biomedically relevant conditions.

Fluorescence-Based Detection of Sumatriptan Using Green Carbon Quantum Dots from Nerium oleander: Optimization via DOE and Machine Learning

Machine Learning–Enhanced Localized Surface Plasmon Resonance Sensing: From Spectra to Intelligent Devices

Localized surface plasmon resonance (LSPR) sensing converts perturbations of the local dielectric environment, nanostructure coupling, and resonance line shape into optical observables such as extinction or scattering spectra, resonance shifts, intensity changes, linewidth variation, asymmetry, and time‐resolved trajectories. These readouts support many label‐free chemical and biological assays, but their interpretation can be confounded by weak spectral changes, nanostructure heterogeneity, aggregation, drift, and mass‐transport effects. This review evaluates how machine learning (ML) can improve information extraction when the model is matched to the LSPR measurement physics, data structure, and validation regime. We connect physics‐first features, dimensionality reduction, supervised learning, convolutional neural networks (CNNs), attention models, and physics‐informed strategies to preprocessing, classification, concentration prediction, nanostructure inversion, device optimization, and adaptive measurement. Representative studies are organized by plasmonic modality, observable, inference target, data‐splitting unit, and evidentiary scope. Particular attention is given to task‐dependent physical priors, nonmonotonic Fano responses, near‐field and binding saturation, label uncertainty, chip‐to‐chip variation, mass‐transport‐limited kinetics, and mechanism‐specific drift. ML can improve estimation precision and analytical discrimination within validated operating ranges, but it cannot recover information absent from the measurement, resolve a non‐identifiable inverse problem without additional observables or priors, or make extrapolation beyond the calibrated domain inherently safe.

Excitonic and Optical Transduction Mechanisms in Quantum Dot Sensors for Environmental Pollutant Detection

The accelerating contamination of global ecosystems by heavy metal ions, per- and polyfluoroalkyl substances (PFASs), microplastics and nanoplastics (MNPs), and emerging contaminants demands sensing technologies that are rapid, sensitive, selective, and field-deployable. Quantum dots (QDs) have emerged as leading candidates for environmental sensing; however, their performance is often interpreted empirically rather than through a unified understanding of the underlying excitonic physics. This narrative review presents a mechanistically integrated framework for QD-based environmental sensing, establishing the exciton, the spatially confined electron–hole quasiparticle, as the primary signal carrier in the most analytically powerful QD sensing modalities. A critical distinction is drawn between three categories of signal-generating processes: genuine excitonic transduction (photoinduced electron transfer, trap-state modulation, FRET, charge-transfer exciton formation, and binding energy modulation); non-excitonic optical phenomena, including the inner filter effect and light scattering, which are frequently misattributed as excitonic responses; and partially excitonic processes such as certain electrochemiluminescence pathways. Exciton fundamentals, confinement effects, and the influence of defects, dopants, and surface states are examined across carbon, chalcogenide, perovskite, and III–V QD families. A Defect–Exciton Energy Map is introduced as a rational design tool linking defect characteristics to excitonic response regime and sensing modality. Application of the mechanistic framework to heavy metal ions, PFASs, microplastics, and emerging contaminants demonstrates that sensing performance differences are mechanistically predictable from excitonic parameters rather than being arbitrary outcomes of materials choice. Benchmarking against competing platforms identifies conditions under which QD sensors offer genuine advantages. The roles of density functional theory, molecular dynamics, and machine learning in enabling rational sensor design are assessed. Key challenges, including stability, real-sample validation, standardisation, and toxicity, and future directions, including QD/two-dimensional material heterostructures and circular economy carbon QD platforms, are identified.