Search Results (58)

Spectroelectrochemistry (SEC) provides unique information-rich datasets by coupling molecular spectroscopic fingerprints with electrochemical activity. Despite its richness, direct machine-learning (ML) analysis of SEC data under realistic experimental constraints and scarcity remains unexplored. This work examines how much spectroscopically encoded electrochemical information can be learned from minimal SEC training data in a chemically reversible two-electron redox system, and how model choice interacts with limited experimental diversity across scan rate. Using purely experimental SEC datasets collected at four scan rates (2, 3, 5, and 7 mV/s), partial least squares (PLS) and convolutional neural networks (CNNs) regressors are evaluated under several SEC-level training, validation, and test configurations. Model performance is assessed across three targets of increasing physical complexity, including species concentrations, derivative cyclic voltabsorptometry (DCVA) current, and experimental cyclic voltammetry (CV) current. Under single-SEC training, both models achieve the expected near-quantitative concentration prediction (R² ~0.99), while performance decreases for DCVA (R² ~0.93) and most substantially for CV current (R² ~0.78), reflecting the progressively weaker and more indirect encoding of these targets within the absorbance data. Introducing minimal experimental diversity with only two distinct training SEC datasets enables both PLS and CNN models to generalize strongly to an unseen third SEC dataset, achieving maximum CV R² values approaching ~0.98 in the most favorable configurations. CNN models extend the apparent linear performance ceiling observed for PLS by capturing localized, scan-rate-conditioned nonlinear correlations between spectral evolution and the experimentally measured CV response, yielding improved waveform reconstruction and greater robustness to training SEC dataset selection. These results demonstrate that, within the present chemically reversible and spectroscopically well-resolved SEC system, high-fidelity prediction of electrochemical targets can be achieved without large datasets when limited but strategically selected electrochemical diversity is introduced. SEC dataset linearity is further shown to be target-dependent and becomes operationally meaningful only when scan-rate space is sufficiently sampled. More broadly, this work establishes a controlled framework for investigating ML-enabled SEC dataset analysis under experimentally scarce conditions and provides guidance for experimental design and calibration in low-data spectroelectrochemical settings. Full article

Electronic nose (EN) technology, based on electrochemical sensor arrays, has emerged as a rapid and objective tool for aroma characterization in food systems. This study investigated factors influencing cat food preference using physicochemical analysis, texture measurement, preference testing, and EN technology. Nine (9) commercial cat foods from three brands (A: premium; B and C: medium price) with different flavors were evaluated. Proximate analysis revealed no significant differences (p > 0.05) in most nutrients, except crude fiber (CF) (p < 0.05), which was higher in lower-priced cat foods. Shear force showed a positive correlation with consumption (r = 0.72), while CF was negatively correlated (r = −0.52). Preference tests indicated that Brand A was most preferred, followed by Brand C, while Brand B was least preferred. Principal component analysis (PCA) identified variability in individual cat preferences, and one outlier cat was excluded. Discriminant analysis of EN data showed clear separation by brand rather than flavor, suggesting brand-related aroma profiles can be monitored rapidly through the digital odor fingerprint. Cat food acceptance is mainly driven by texture, CF content, and aroma rather than macronutrient composition. The results of the EN evaluation of cat food samples revealed the same group similarities and differences as the 8-month preference test performed with cats. Similar to this study, EN classification models can be developed using food preference data and the digital aroma fingerprints of foods. After validations, the EN models can be used as an effective tool to monitor the quality and assess new diets according to the known preferred odor fingerprint. Full article

Polyethylene (PE) microplastics are increasingly recognized as a critical environmental and food-safety concern; however, routine monitoring remains limited by conventional methods that are labor-intensive, time-consuming, and difficult to translate into rapid, on-site screening. Here, we report a machine learning-guided electrochemical fingerprinting platform for rapid PE microplastic detection using a chitosan–PE interfacial film coupled with electrochemical impedance spectroscopy (EIS) and coulometry. The platform generated concentration-dependent electrical fingerprints in artificial ocean water, captured through Bode, Nyquist, and charge–time responses. Quantification was achieved across 1–256 ng/mL with strong linearity (R² = 0.976) and an ultralow LoD of 0.1 ng/mL, demonstrating high analytical sensitivity. Practical applicability was validated through spike–recovery in ocean water (R² = 0.967) and shrimp-derived matrices with matrix-matched normalization, yielding recoveries of 90–105% across low, mid, and high spike levels. Under the tested particle set, PE produced stronger responses than non-target polypropylene (PP) and polystyrene (PS), supporting empirical polymer discrimination. Machine learning classification using impedance-derived features achieved an AUC = 0.98, with 100% correct identification of Low and 95.24% correct identification of High samples. Overall, this electrochemical–ML framework enables rapid, sensitive, and matrix-tolerant PE microplastic screening in environmental water and seafood-related matrices, offering a promising pathway toward portable microplastic monitoring. Full article

Molecular-scale detection based on quantum tunnelling is promising for molecular electronics and high-sensitivity analysis, owing to its sensitivity to molecular structure and energy levels. However, conventional two-electrode tunnelling measurements suffer from overlapping conductivity of different molecules, limiting molecular discrimination in complex systems. To address this, we propose an electrochemical-gate-controlled nanoscale tunnelling strategy that expands the two-electrode system to a three-electrode configuration via a tunable gate potential, enabling the differentiation of distinct molecules at near-single-molecule sensitivity. Scanning the gate potential under constant tunnelling bias modulates the alignment between molecular orbitals and the electrode Fermi level, altering the statistical characteristics of molecular tunnelling transport. Experimental results show that target molecules induce a bimodal distribution of tunnelling current (background and molecule-correlated channels), with the second peak exhibiting distinct gate potential dependence. Comparative analysis of ascorbic acid (AA), acetylcholine (ACh), and uric acid (UA) reveals unique trajectories of characteristic peaks with gate potential, forming an electrochemical gate response fingerprint. This gate-dependent conductance trajectory provides a novel statistical dimension for molecular recognition, enabling differentiation of distinct molecules. Full article

Short-chain fatty acids (SCFAs) are key biomarkers of gut microbiota activity; however, routine quantification in fecal samples relies largely on chromatography, which is instrument-intensive and throughput-limited chromatography techniques. Herein, we present a rapid machine-learning-assisted electroanalysis platform for SCFAs profiling that integrates a disposable three-electrode planar gold chip with voltammetric fingerprinting and artificial neural network (ANN)-based signal decoupling. To generate orthogonal chemical information and improve the discrimination of structurally similar species, a dual pretreatment strategy combining acid-catalyzed esterification and alkaline dissociation was employed prior to electrochemical analyses. Differential pulse voltammetry (DPV) and cyclic voltammetry (CV) were employed to acquire high-dimensional fingerprints, from which current-, potential-, and area-based descriptors were extracted using a cross-information feature strategy. A hierarchical modeling framework improved total SCFAs prediction by incorporating ANN-predicted propionate and butyrate concentrations as auxiliary inputs. While linear calibration was achievable in standard mixtures, direct linear models performed poorly in real fecal matrices due to strong sample-dependent matrix interference. In contrast, the ANN captured nonlinear relationships among multifeature inputs and suppressed matrix effects. Validation against gas chromatography–mass spectrometry in an independent fecal test cohort (n = 30) demonstrated excellent agreement and low prediction errors, with mean absolute error/root mean square error values of 0.063/0.072 mM (propionic acid), 0.029/0.034 mM (butyric acid), and 0.135/0.202 mM (total SCFAs). The DPV/CV acquisition requires only minutes per sample, whereas pretreatment takes 1~3 h depending on the target route but can be performed in parallel for batch processing; thus, overall throughput is determined mainly by batch pretreatment rather than per-sample instrument time. This electrochemical–ANN workflow provides a portable, high-throughput alternative to chromatography for fecal SCFAs profiling in clinical screening and microbiome research. Full article

Ground clove bud adulteration with cheaper materials, such as clove stem and soil, poses a significant threat to spice quality and consumer trust. This study introduces a novel, alternative analytical method for the authentication and detection of adulteration in ground clove bud samples. The approach combines voltammetric fingerprinting using a multi-walled carbon nanotube-modified electrode with robust chemometric analysis. Cyclic voltammetry of clove bud samples revealed anodic peaks above +0.5 V and a smaller cathodic peak between +0.5 and −0.3 V vs. Ag/AgCl, suggesting the presence of electroactive compounds. Voltammograms were obtained for authentic clove bud samples sourced from three major Indonesian production regions (South Sulawesi, North Maluku, and East Java), showing varying redox peak intensities. Chemometric analysis, specifically Partial Least Squares Discriminant Analysis (PLS-DA), was successfully employed to differentiate clove bud samples by geographical origin, and Principal Component Analysis (PCA) was used to discriminate authentic clove bud samples from adulterants. Furthermore, Partial Least Squares Regression (PLSR) was utilized to quantify adulteration levels, predicting adulterant concentration (10–100% w/w) using electrochemical signal intensities. The PLSR method exhibited strong linearity between observed and predicted values, confirming its robustness. This proposed method offers a simple, portable, and practical approach for the quality control of ground clove bud. The combination of rapid voltammetric measurement and chemometric modelling provides a valuable and practical tool to prevent fraud and ensure the integrity of the spice trade. Full article

In this work, we present the introductory methodology for a graphene field-effect transistor (GFET)-based platform for probing the electrochemical fingerprints of amino acids, designed to enable stable and controlled surface chemistry and electrochemical measurements toward peptide and protein sequencing. We begin with a focused conceptual review that motivates electrochemical fingerprinting as a strategy for amino acid and peptide identification and contextualizes this approach within recent advances in protein manipulation relevant to sequencing. We then describe a graphene functionalization protocol that facilitates the directional attachment of amino acids onto the graphene surface. This surface chemistry is quantitatively characterized through surface plasmon resonance (SPR), yielding surface densities in the order of 10¹² molecules/cm². The same functionalization protocol enables in situ peptide synthesis directly on graphene, as demonstrated by the successful synthesis of a model tripeptide. To support electrochemical interrogation, we developed three complementary platforms for sensor preconditioning, surface functionalization, and titration-based electrochemical measurements, compatible with both aqueous and organic solutions. Preliminary stability measurements indicate a Dirac point drift below 10 mV over 45 min. Altogether, this work establishes the experimental foundations for electrochemical amino acid and peptide fingerprinting using GFET sensors and provides a framework for the future development of electrochemically enabled protein sequencing technologies. Full article

Accurate prognosis of the remaining useful life (RUL) for lithium-ion batteries is critical for mitigating range anxiety and ensuring the operational safety of electric vehicles. However, existing data-driven methods often struggle to maintain robustness when transferring from controlled laboratory conditions to complex, sensor-limited, real-world environments. To bridge this gap, this study presents U-H-Mamba, a novel uncertainty-aware hierarchical framework trained on a massive hybrid repository comprising over 146,000 charge–discharge cycles from both laboratory benchmarks and operational electric vehicle datasets. The proposed architecture employs a two-level design to decouple degradation dynamics, where a Multi-scale Temporal Convolutional Network functions as the base encoder to extract fine-grained electrochemical fingerprints, including derived virtual impedance proxies, from high-frequency intra-cycle measurements. Subsequently, an enhanced Pressure-Aware Multi-Head Mamba decoder models the long-range inter-cycle degradation trajectories with linear computational complexity. To guarantee reliability in safety-critical applications, a hybrid uncertainty quantification mechanism integrating Monte Carlo Dropout with Inductive Conformal Prediction is implemented to generate calibrated confidence intervals. Extensive empirical evaluations demonstrate the framework’s superior performance, achieving a RMSE of 3.2 cycles on the NASA dataset and 5.4 cycles on the highly variable NDANEV dataset, thereby outperforming state-of-the-art baselines by 20–40%. Furthermore, SHAP-based interpretability analysis confirms that the model correctly identifies physics-informed pressure dynamics as critical degradation drivers, validating its zero-shot generalization capabilities. With high accuracy and linear scalability, the U-H-Mamba model offers a viable and physically interpretable solution for cloud-based prognostics in large-scale electric vehicle fleets. Full article

Escherichia coli (E. coli) is a well-established indicator of faecal pollution and a potent pathogen linked to numerous gastrointestinal and systemic illnesses. Ensuring public safety requires rapid and sensitive detection methods capable of real-time, on-site deployment. Many conventional techniques are either laborious, time-intensive, costly, or require complex infrastructure, limiting their applicability in field settings. Raman spectroscopy offers label-free molecular fingerprinting; however, its inherently weak scattering signals restrict its effectiveness as a standalone technique. Surface-Enhanced Raman Spectroscopy (SERS) overcomes this limitation by exploiting plasmonic enhancement from nanostructured metallic substrates—most commonly gold, silver, copper, and aluminium. Despite the commercial availability of SERS-active substrates, challenges remain in achieving high reproducibility, long-term stability, and true field applicability, necessitating the development of integrated lab-on-chip platforms and portable, handheld Raman devices. This review critically examines recent advances in SERS-based E. coli detection across water and perishable food products with particular emphasis on the evolution of SERS substrate design, the incorporation of biosensing elements, and the integration of electrochemical and microfluidic systems. By contrasting conventional SERS approaches with next-generation biosensing strategies, this paper outlines pathways toward robust, real-time pathogen detection technologies suitable for both laboratory and field applications. Full article

Raman spectroscopy has become a key tool for resolving the molecular behavior of interfaces due to its non-invasiveness, fingerprinting ability and in situ detection advantages. Surface-enhanced Raman scattering (SERS) and its derivative techniques (including SHINERS and TERS) have significantly overcome the challenges of weak interfacial signals and strong water interference through the synergistic effect of electromagnetic field enhancement and chemical enhancement. They have realized highly sensitive molecular detection at various interfaces such as solid–liquid, gas–liquid, water–oil, and so on. Despite the challenges of substrate stability and signal quantization, the deep integration of multi-technology coupling and theoretical computation will further promote the breakthrough of this technology in interface science. In this review, we systematically review the applications of Raman spectroscopy and SERS techniques in interface resolution, including key research directions such as analyzing interfacial molecular structures, detecting material reactions at water–oil interface, and tracking the evolution of electrochemical interfacial species, as well as exploring the technological bottlenecks and future development directions. Full article

Recent advancements in carbon nanotubes and graphene have driven significant research into other low-dimensional materials, with phosphorus-based materials emerging as a notable area of interest. Phosphorus nanowires and thin sheets show promise for applications in devices such as batteries, photodetectors, and field-effect transistors. However, the presence of multiple allotropes of phosphorus complicates their characterization. Accurate identification of these allotropes is essential for understanding their physical, optical, and electronic properties, which influence their potential applications. Researchers frequently encounter difficulties in consolidating literature for the confirmation of the structure of their materials, a process that can be time-consuming. This minireview addresses this issue by providing a comprehensive, side-by-side comparison of Raman and X-ray diffraction characteristic peaks, as well as electron microscopic images and lattice spacings, for the various phosphorus allotropes. To our knowledge, this is the first compilation to integrate all major structural fingerprints into unified summary tables, enabling rapid cross-referencing. This resource aims to support researchers in accurately identifying phosphorus phases during synthesis and device fabrication workflows. For example, distinguishing between red phosphorus polymorphs is crucial for optimizing anode materials in sodium-ion batteries, where electrochemical performance is phase-dependent. Full article

Due to its unique molecular fingerprinting capability and multiplex detection advantages, surface-enhanced Raman scattering (SERS) has shown great application potential in the field of biological analysis. However, the weak signal intensity and large background interference significantly limited the application of SERS in biosensing and bioimaging. Loading a large amount of Raman molecules with signal in the silent region on the hotspots of the electromagnetic field of the SERS substrate can effectively avoid severe background noise signals and significantly improve the signal intensity, making the sensitivity and specificity of SERS detection remarkably improved. To achieve this goal, a new SERS signal-amplification strategy is herein reported for background-free detection of Cu²⁺ by using Raman-silent probes loaded on cabbage-like gold microparticles (AuMPs) with high enhancement capabilities and single-particle detection feasibility. In this work, carboxyl-modified AuMPs were used to enable Cu²⁺ adsorption via electrostatic interactions, followed by ferricyanide coordination with Cu²⁺ to introduce cyano groups, therefore generating a stable SERS signal with nearly zero background signals owing to the Raman-silent fingerprint of cyano at 2137 cm⁻¹. Based on the signal intensity of cyano groups correlated with Cu²⁺ concentration resulting from the specific coordination between Cu²⁺ and cyanide, a novel SERS method for Cu²⁺ detection with high sensitivity and selectivity is proposed. It is noted that benefiting from per ferricyanide possessing six cyano groups, the established method with the advantage of signal amplification can significantly enhance the sensing sensitivity beyond conventional approaches. Experimental results demonstrated this SERS sensor possesses significant merits towards the determination of Cu²⁺ in terms of high selectivity, broad linear range from 1 nM to 1 mM, and low limit of detection (0.1 nM) superior to other reported colorimetric, fluorescence, and electrochemical methods. Moreover, algorithm data processing for optimization of SERS original data was further used to improve the SERS signal reliability. As the proof-of-concept demonstrations, this work paves the way for improving SERS sensing capability through the silent-range fingerprint and signal amplification strategy, and reveals SERS as an effective tool for trace detection in complex biological and environmental matrices. Full article

Cardiac troponin I (cTnI) is a crucial biomarker for the early detection of acute myocardial infarction (AMI), playing a significant role in cardiac health assessment. Molecularly imprinted polymers (MIPs) are valued for their stability, ease of fabrication, reusability, and selectivity. However, using the analyte as a template can be costly, especially if the analyte is expensive. In such cases, a dummy template (DT) with similar chemico-physical properties can be useful. This study aimed to design a DT-MIP for cTnI detection using cytochrome c (Cyt c) as the template, combining computational and experimental approaches. Molecular docking identified binding sites on Cyt c and cTnI for poly(o-phenylenediamine) (5PoPD) pentamers. Interactions and binding energies were examined using all-atom molecular dynamics (MDs) simulations and structural interaction fingerprint (SIFt) calculations. A DT-MIP-modified electrode for cTnI detection was prepared by electropolymerizing o-PD in the presence of Cyt c as a dummy template. Electrochemical techniques monitored the electropolymerization, template removal, and binding of the target analyte. The experimental results showed that the DT-MIPs exhibited a high binding affinity for cTnI, consistent with the binding energies observed in MD simulations. The satisfactory correlation between experimental and computational results validated our model-based approach for the rational design of dummy template molecularly imprinted polymers. Full article

The appearances of Schisandrae Sphenantherae Fructus (SSF) and Schisandrae Chinensis Fructus (SCF) are very similar. Thus, being able to distinguish between SSF and SCF is useful for the quality control of these herbal medicines. In this study, two kinds of electrochemical fingerprint analyses using voltammetry or HPLC with electrochemical detection (HPLC-ECD) were developed in combination with multivariate analysis for discriminating between SSF and SCF. The data sets of the oxidation current values from voltammograms of SSF and SCF samples ranging from +0.5 to +1.0 V were applied to perform a partial least squares discriminant analysis (PLS-DA). Moreover, the data sets of the current heights of the characteristic target peaks on the chromatograms at an applied potential of +0.9 V were also applied to perform PLS-DA. In each PLS-DA obtained from the voltammograms and chromatograms, the scores for the SSF samples were plotted on a different region compared with the scores for the SCF samples. Considering the results of the cross-validation, the HPLC-ECD clearly discriminated between the SSF and SCF samples when compared with the voltammetry. In conclusion, our results show that the present electrochemical fingerprint analyses coupled with PLS-DA are useful as a means for discriminating between the SSF and SCF samples. Full article

A group of bithiophenyl compounds comprising the cyanoacrylate moiety were designed and successfully synthesized. The optical, (spectro)electrochemical, and aggregation-induced emission properties were studied. DFT calculations were used to explain the reaction’s regioselectivity and to determine the molecules’ energy parameters (i.e., band gaps, HOMO levels, and LUMO levels). The aggregation-induced emission of compounds has been studied in the mixture of DMF (as a good solvent) and water (as a poor solvent), with different water fractions ranging from 0% to 99%. It has been shown that there are differences in the physicochemical properties of the obtained compounds due to the length of the alkyl chain in the ester group. Investigated derivatives were tested for their potential use in visualizing latent fingerprints and electrochromic materials. Full article