Point-of-care testing (POCT) demands rapid, user-friendly solutions for instant medical diagnosis. Traditional colorimetric whole-blood analysis faces challenges from red blood cell and hemoglobin interference, often necessitating complex pretreatment. Herein, we introduce a three-dimensional (3D) nanoporous hydrogel-integrated portable platform designed for pretreatment-free, rapid multiplexed detection of small-molecule biomarkers directly from whole blood. The platform utilizes a polystyrene Eppendorf (EP)-tube as a miniaturized laboratory, featuring a TiO2-sodium alginate hydrogel-modified cap with a 3D nanoscale porous architecture. This innovative structure serves dual roles: embedding enzymatic systems for cascade reactions and excluding macromolecules (e.g., hemoglobin, erythrocytes) through size-selective exclusion. Small-molecule biomarkers (e.g., glucose, uric acid, lactate) diffuse into the hydrogel matrix, triggering concentration-proportional colorimetric signals via enzymatic cascade reactions. A 15-min "flip-and-scan" protocol-comprising tube inversion for biomarker diffusion and smartphone RGB analysis for quantitative readout-enables simultaneous quantification of three biomarkers with detection limits of 0.11 mM for glucose, 16 μM for uric acid, and 20 μM for lactate. The clinical validation with diverse patient samples (diabetic, gout, and composite metabolic cases) demonstrates that the tri-marker detection strategy enables precise differential diagnosis of overlapping metabolic syndromes by distinguishing biomarker patterns. Clinical validation with 15 diverse patient samples (5 diabetic patients, 5 gout patients, and 5 patients with composite metabolic abnormalities) demonstrated strong concordance with a commercial clinical biochemistry analyzer, with Pearson correlation coefficient > 0.95 and negligible bias in Bland-Altman analysis with all data points falling within the 95% limits of agreement. Compared with conventional microfluidic and paper-based sensors, this approach eliminates pretreatment steps, reduces assay time, ensures closed-loop biosafety, and prevents cross-contamination, offering a versatile and accessible analytical platform that represents a significant step toward practical multiplexed biomarker detection in POCT.

Microfluidic paper-based analytical devices (μPADs) are attractive platforms for on-site analysis; however, quantitative analysis remains challenging because sampling strongly influences analytical performance. Existing sampling methods often require precise volumetric sampling or operator-dependent handling, which involves an inherent trade-off between analytical simplicity and quantitative reliability. To overcome this limitation, herein, we propose the concept of a quantitative assay using an immersion-type hydrogel-integrated microfluidics glass fiber-based analytical device (IHμGFAD). The IHμGFAD consisted of a reagent-encapsulated hydrogel for reaction/detection with a glass fiber channel for sampling. The combination of the hydrogel with the glass fiber channel was realized by exploiting the self-adhesive property of hydrophobic patterns fabricated using a liquid crystal display 3D printer. The immersion-based quantitative assay using the IHμGFAD was demonstrated with a glucose oxidase/horseradish peroxidase-based glucose colorimetric assay as a model system. The limit of glucose detection (3σ) was estimated to be 68 μM. The immersion-based assay was also applied to the quantification of glucose in human serum controls and pooled human serum, demonstrating its potential applicability to real-sample analysis. The immersion-based assay provides a μGFAD-based analytical method that achieves both practical user-friendliness and reliable quantitative performance, and it contributes to the advancement of on-site quantitative analysis in a wide range of fields, including healthcare, environmental monitoring, and food safety.

The Solid-Phase Microextraction (SPME) is a minimally invasive extraction technique that has gained interest in environmental studies. In this paper, we developed an SPME-LC-MS/MS protocol under ex vivo conditions to extract carbamazepine (CBZ) and its main metabolite, carbamazepine-10,11-epoxide (CBZE) from Chlamys islandica solid tissues. Optimization of SPME included selection of the stationary phase, desorption solution, desorption and extraction times. Under optimized conditions, we utilized commercially available C18 fibers, a 10 min static extraction and a 10 min desorption of CBZ and CBZE performed into 50 μL of ACN:H2O (80:20, v/v) mixture. The linear range for CBZ was 2.5-20 ng g[-1] of wet mass with R[2] = 0.9900; for CBZE, the linear range was 20-1000 ng g[-1] of wet mass (R[2] = 0.9743). LLOQ levels were 2.5 ng g[-1] and 20 ng g[-1] of wet mass for CBZ and CBZE, respectively. A 7-day exposure experiment was performed as a proof-of-concept, in which bivalves were exposed to CBZ at 1000 ng L[-1]. Ex vivo SPME enabled CBZ quantification in all four tissues. Mean concentrations increased from day 1 to day 7 in muscle, gonads, and the digestive system, while remaining stable in gills. Similar temporal trends were observed using solid-liquid extraction (SLE), although lower concentrations were obtained. The results demonstrate that SPME enables rapid and minimally invasive determination of pharmaceuticals in marine organisms and represents a promising approach for future in vivo monitoring of contaminant bioaccumulation.

This study presents a novel design of a millifluidic chip that integrates liquid phase microextraction with analyte detection through an electrochemical sensor. Upon incorporating the sample solution that includes the model analyte, morphine, into the donor part of the millifluidic chip, passive diffusion occurs as it traverses the supported liquid membrane, influenced by the pH gradient. The innovative sensor utilizes a platinum electrode (Pt) that is modified with a synthesized covalent organic framework (COF) based on 4,4'-methylenedianiline and 4,4',4''-((1,3,5-triazine-2,4,6-triyl) tris(oxy)) tribenzaldehyde (MDA-Trz-COF, referred to as MDA-Trz-COF/Pt. Following the extraction duration in the acceptor part, a differential pulse voltammetry determination of morphine was conducted. The parameters influencing the electrochemical-liquid phase microextraction (LPME) chip were refined through one-variable-at-a-time and central composite design methodologies. In ideal circumstances, the maximum current responses were observed during the oxidation of morphine on the MDA-Trz-COF/Pt electrode versus various concentrations of morphine within two distinct linear ranges of 5.0-120.0 and 120.0-700.0 pmol L[-1], with a limit of detection of 1.5 pmol L[-1] and a limit of quantification of 5.0 pmol L[-1], respectively. The efficacy of the proposed method for the preconcentration and quantification of morphine in urine samples was thoroughly examined.

Laser-induced breakdown spectroscopy (LIBS) has emerged as a promising technique for rapid elemental analysis in industrial manufacturing owing to its capability for real-time, non-contact, and multi-element detection. However, severe spectral noise, matrix effects, and spectral overlap substantially limit the robustness and reliability of conventional LIBS data analysis under complex industrial conditions. To address these challenges, a knowledge-guided multi-objective deep learning framework is proposed for intelligent LIBS-based magnesium alloy identification. The proposed approach integrates hierarchical spectral feature extraction with Grey Wolf Optimizer (GWO)-based adaptive hyperparameter optimization while incorporating spectroscopy-informed prior knowledge into the feature learning process to improve interpretability and generalization. Unlike conventional optimization strategies that focus solely on classification accuracy, the proposed framework simultaneously optimizes classification accuracy, noise robustness, and computational efficiency. Extensive experiments, including multi-level noise evaluation, ablation analysis, Grad-CAM visualization, inference latency measurement, and stratified five-fold cross-validation, demonstrate the superiority of the proposed method. The optimized model achieves an average classification accuracy of 99.90%, exhibits exceptional robustness under severe spectral interference, and reaches saturated performance within 150 training epochs under a maximum training setting of 200 epochs with early-stopping strategy, and maintains an inference latency of approximately 45 ms. Furthermore, the extremely low cross-validation standard deviation (0.0002) confirms excellent model stability and generalization across different data partitions. These results demonstrate that the proposed strategy provides a robust, interpretable, and practically deployable solution for intelligent LIBS analysis in industrial material identification.

Surface-enhanced Raman scattering (SERS) methods offer high sensitivity for uric acid (UA) detection, but efficient molecular enrichment and reliable analysis in complex biological matrices remain challenging. Here, we developed an Ag/Cu-TCPP SERS platform for noninvasive determination of UA in saliva. The platform combined the electromagnetic enhancement provided by Ag nanoparticles (Ag NPs) with the molecular enrichment capability of Cu-TCPP. Density functional theory calculations further indicated that UA can be enriched on Cu-TCPP through weak Cu···O quasi-coordination between the UA carbonyl oxygen and exposed Cu centers, accompanied by interfacial charge redistribution. Consequently, the platform achieved a low detection limit of 0.04 μM and a wide linear range of 0.1-1000 μM. Beyond conventional single-point determination, the platform enabled time-resolved assessment of salivary UA concentration, revealing temporal fluctuations under physiological and feeding-related conditions. These results highlight the potential of MOF-integrated SERS platforms for noninvasive monitoring of small-molecule biomarkers.

To overcome the limitations of conventional electrocatalytic wastewater treatment-including high external energy consumption, low solid-liquid interfacial charge density, unstable electrical output, and severe charge backflow-we developed an excited tribo-contact electrocatalytic (E-TCE) system. This system integrates a charge excitation module and a rectified energy-storage unit into a conventional TCE setup, enabling self-powered operation. By coupling a PTFE@MoS2 composite membrane with the bulk effect, the charge transport pathway is extended from the membrane surface into the material interior, significantly enhancing interfacial charge utilization. Under optimized conditions (dual-electrode configuration, 68 nF capacitance, 0.5 g/L MoS2 loading, 20 cm droplet height, 60° membrane inclination, and 15 mg/L crystal violet (CV)), the E-TCE system achieved a CV degradation efficiency of 94.3%, with a current output of 8.3 nA and a charge output of 9.4 nC-substantially outperforming the original TCE system. The system also demonstrated broad-spectrum efficacy, degrading methyl orange (MO), rhodamine B (RhB), and tetracycline (TC), and maintained >85% CV removal after five consecutive cycles. Radical scavenging and EPR analyses identified ·O2- and ·OH as the primary reactive species, while LC-MS-based degradation pathway and ecotoxicity assessments confirmed progressive detoxification. This work presents a self-powered, bulk-effect-enhanced interfacial charge regulation strategy for sustainable water remediation.

Glutathione (GSH), a ubiquitous biothiol, is critical in physiological processes, and abnormal fluctuations in its level are intimately associated with various diseases. However, real-time in vivo monitoring of GSH within tissues, such as the tumor microenvironment (TME), is fundamentally hindered by severely electrode biofouling and electrochemical interference from structural analogs. Herein, we report a novel target-responsive "Signal-Down" electrochemical biosensor based on an anti-biofouling hydrogel for GSH detection, which elegantly integrates robust anti-biofouling capabilities with highly specific target recognition. This sensing interface features a highly hydrated polyacrylamide (PAM) network, crosslinked by disulfide bonds and loaded with ferrocene/β-cyclodextrin (Fc@β-CD) electroactive probes. Upon exposure to GSH, the highly specific thiol-disulfide exchange reaction triggers the macroscopic degradation of the Fc-hydrogel. This spatial disruption drives the rapid outward diffusion of the redox probes, translating microscopic molecular recognition into a dramatic attenuation of the electrochemical signal. This biosensor not only successfully differentiated abnormal intracellular GSH levels in B16 melanoma cells from those in normal L929 fibroblasts in vitro, but also achieved in situ detection of pathological redox dysregulation in a melanoma-bearing mouse model. Ultimately, by uniquely coupling macroscopic material degradation with signal attenuation, this work establishes a reliable strategy for continuous, interference-free biosensing in highly complex and fouling-prone biological matrices.

Biomass is one of the key parameters to be monitored during avilamycin fermentation. Currently, periodic sampling is commonly employed for biomass detection, which often entails significant manpower and cost expenditure. Therefore, it is crucial to develop a method capable of real-time, online monitoring of biomass changes during avilamycin fermentation. This study selected wet weight as the biomass measurement indicator during avilamycin fermentation, and employed online near-infrared spectroscopy (NIRS) technology to perform rapid, non-destructive monitoring of wet weight content throughout the fermentation process. Comparing the models' performance based on three algorithms, including partial least squares regression (PLSR), principal component regression (PCR) and support vector regression (SVR), it was confirmed that the PLSR model demonstrated optimal performance. After outlier removal, preprocessing method selection, and feature wavelengths screening, the results indicated that the dynamic biomass prediction model constructed using the SG+1[st] Der-i-PLSR method exhibited the optimal predictive performance. Within the validation set, the prediction set determination coefficient (R[2]p), root mean square error of prediction (RMSEP), residual predictive deviation (RPD), and standard error of prediction (SEP) were 0.9319, 21.19, 3.351 and 20.98, respectively. For wet weight content ranging from 109.5 to 436.5 g/L, the intuitive metric mean absolute error (MAE) was 16.30 g/L after external validation across five fermentation batches. The foregoing indicates that this model holds promising application prospects for online non-destructive biomass monitoring during avilamycin fermentation. It will lay the foundation for process optimization and control in the Industrial-scale production of avilamycin.

Rapid functional tools for assessing antimony susceptibility in Leishmania are needed to strengthen resistance surveillance across human and veterinary settings. We developed a real-time oxygen-consumption assay using the Resipher analyzer and evaluated normalized oxygen-consumption rate (OCR) as a functional correlate of antimony (Sb) resistance in Leishmania infantum and Leishmania donovani. A density of 1 × 10[7] parasites/mL and a 48-h readout provided the most robust analytical window, and rotenone markedly reduced OCR, demonstrating assay responsiveness to respiratory perturbation. Antimony-resistant laboratory lines showed lower OCR/proliferation ratios than the wild-type strain. This phenotype was reproduced in ten canine L. infantum field isolates and ten human L. donovani clinical isolates. OCR/proliferation ratio was inversely correlated with promastigote (L. infantum: r = -0.8930, p = 0.0008; L. donovani: r = -0.8470, p = 0.0019) and intracellular-amastigote Sb EC50 values (L. infantum: r = -0.8787, p = 0.0008; L. donovani: r = -0.7964, p = 0.0058). Under isolate-specific Sb EC90 exposure, resistant isolates accumulated less ROS, which was positively associated with OCR/proliferation ratio (L. infantum: r = 0.6927, p = 0.0390; L. donovani: r = 0.7560, p = 0.0114). Low-OCR isolates were also less sensitive to rotenone and antimycin A, indicating broader bioenergetic remodeling. Normalized OCR is a functional correlate of Sb susceptibility and a complementary phenotypic screening approach for One Health surveillance. Future studies incorporating larger and more genetically diverse isolate collections, prospective susceptibility testing, and inter-laboratory standardization will enable the definition of robust analytical thresholds and facilitate implementation in resistance-surveillance programs.

Rapid determination of U(VI) in uranium mining wastewater is essential for environmental risk assessment and wastewater treatment control. Herein, we developed a compact 3D-printed optofluidic platform that translates uranium colorimetric chemistry into an automated and portable flow detection system. The platform integrates programmable flow injection, a serpentine mixer, a customized optofluidic detection module, fixed optical coupling, and spectral acquisition to enable reproducible mixing and stable optical signal collection. Key optical, fluidic, and reaction parameters were systematically optimized to improve signal stability and quantitative reliability. Under optimized conditions, the platform showed a linear response of 0.05 - 1.00 mg L[-1], with a limit of detection of 0.013 mg L[-1], precision better than 5%, and stable performance during repeated measurements. The platform was further validated using uranium mining wastewater, yielding results consistent with ICP-OES and recoveries of 97.7-105.9%. This work provides a practical route for automated field monitoring of uranium containing wastewater and bridges the gap between laboratory colorimetric assays and deployable analytical hardware.

The reliable on-site detection of methylglyoxal (MGO) is increasingly acknowledged as a clinically relevant indicator of carbonyl stress and the risk of diabetes-related complications. However, this remains challenging due to matrix effects, structurally related interferents, and the absence of reaction-based, self-calibrated sensing platforms. This work presents a dual-emission europium metal-organic framework (Eu-DADP/DPA) engineered with 5,6-diamino-1,10-phenanthroline (DADP) and dipicolinic acid (DPA) as ligands for the sensitive and selective dual-mode detection of MGO. DPA can sensitize Eu[3+] for emission due to its antenna effect, while DADP cannot. Consequently, the MOF displayed dual emission at 409 and 632 nm from DADP and Eu[3+] ions, respectively, with excitation at 280 nm. Upon addition of MGO, the two amino groups of DADP undergo cyclization to form a 2-methylquinoxaline-like structure, which suppresses photoinduced electron transfer (PET) and enhances ligand conjugation, resulting in a gradual increase of the 409 nm emission and slight attenuation of the Eu[3+] emission. This response enables ratiometric fluorescence detection of MGO at 0-10 μM with a detection limit of 84 nM. Simultaneously, the MOF acts as a peroxidase-like nanozyme, catalyzing the oxidation of 3,3',5,5'-tetramethylbenzidine (TMB) to a blue product; this activity is inhibited by MGO, providing a convenient colorimetric method for visual or semi-quantitative screening at relatively higher MGO concentrations. The sensor demonstrates selectivity against the tested interferents and satisfactory recoveries in spiked serum samples, with smartphone-based RGB recoveries ranging from 98.2% to 111.3%. This dual-mode Eu-MOF platform combines ratiometric fluorescence sensing with colorimetric detection, providing an analytical approach for MGO measurement with potential for portable readout.

Quantification of low-abundance proteins is critical for biopharmaceutical development, proteomics, and biochemical research; however, existing methods often require trade-offs among sensitivity, sample consumption, and workflow simplicity. Ultraviolet (UV) absorbance at 280 nm remains the most widely used method for routine protein quantification because it is rapid, label-free, and reproducible, but its intrinsic sensitivity limits the analysis of dilute samples, particularly when only microliter sample volumes are available. Here we present a high-sensitivity, label-free method for protein quantification based on native fluorescence imaging (NFI) using a bubble-cell capillary, in which a short, enlarged section is incorporated into an otherwise uniform capillary to enhance fluorescence detection. The enlarged bubble-cell increases the optical interrogation volume, generating a localized fluorescence peak that enables robust peak-based quantification, signal-to-noise analysis, and direct visualization of proteins near the detection limit. Using only 1-2 μL of sample, the method achieved experimentally validated limits of detection of approximately 0.005 mg/mL for bovine serum albumin (BSA) and 0.0025 mg/mL for monoclonal antibodies, representing detection limits approximately one order of magnitude lower than literature-reported values for NanoDrop UV spectrophotometry, while preserving the simplicity of a rapid, label-free, and reagent-free workflow. Method validation demonstrated good reproducibility, satisfactory spike-and-recovery performance, negligible carryover, and reliable quantification over the validated working range. The method was further applied to monitor low-abundance fractions collected during monoclonal antibody charge-variant fractionation, enabling detection of minor protein populations that were not reliably detected using a conventional fluorescence plate reader. The combination of high sensitivity, validated analytical performance, minimal sample consumption, and simple peak-based detection demonstrates the potential of bubble-cell NFI as a practical platform for routine protein quantification and characterization, particularly when sample availability is limited or low-abundance protein species must be analyzed.

Microplastics are a ubiquitous environmental contaminant that can be found virtually every habitat and water source around the world. Therefore, there is great interest in developing efficient and reliable methodologies for the identification and enumeration of microplastics from environmental media. Laser direct infrared spectroscopy (LDIR) utilizes the fingerprint region of infrared (IR) spectra to make identification of plastics and other materials during analysis. However, only pristine spectra of plastics are currently used in the identification process. It has been well documented that the IR spectra of plastics changes considerably upon photochemical weathering, bringing into question whether databases may lead to misidentification of certain polymer types. To test if weathering influences identification of microplastics by LDIR, nine different plastics were irradiated to simulate weathering from 3 to 13 years in the sun. The physical and chemical changes of the plastic were measured by differential scanning calorimetry (DSC) and attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) respectively. The weathered polymers were analyzed by LDIR-transflectance spectroscopy and resulting spectra were loaded into the LDIR matching database. This updated database was used to analyze samples from a local wastewater treatment plant with and without the inclusion of weathered spectra. This manuscript shows the inclusion of weathered transflectance spectra into the LDIR matching database can increase polymer identifications by 9.6 - 10.2% in wastewater samples on average. It also reveals that not all polymers are increased in identification uniformly, as PC and PA show the most sizeable increased spectral matches. However, most polymers tested were able to show increased medium and high-quality matches of weathered polymers using this approach.

In the present study, a bottom-up approach was followed to characterize three recombinant human erythropoietin (rhEPO) biopharmaceuticals: two commercially available drugs (EPO cim and EPO crs) and one currently under clinical evaluation for neurodegenerative diseases (NeuroEPO plus). After tryptic digestion, the resulting glycopeptides were analyzed using two complementary microseparation techniques: capillary electrophoresis and capillary liquid chromatography, both coupled to mass spectrometry (CE-MS and capLC-MS, respectively). These techniques enabled the detection of glycopeptide glycoforms displaying different degrees of sialylation and branching. CE-MS provided faster analyses and higher peak efficiency, whereas capLC-MS offered greater sensitivity, allowing the identification of a larger number of glycoforms. Given the complexity of the generated datasets, unsupervised and supervised multivariate data analysis was applied to explore glycosylation differences among the biopharmaceuticals and facilitate sample classification. Principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) achieved clear differentiation of the three rhEPOs and revealed batch-to-batch variability with both microseparation techniques. In addition, the glycopeptide glycoforms that most strongly contributed to the product differentiation were identified. Specifically, EPO cim and EPO crs displayed higher relative abundances of tri- and tetra-sialylated structures, whereas NeuroEPO plus was characterized by a greater proportion of low-sialylated glycoforms. Moreover, EPO cim and EPO crs mainly differed in the relative abundance of N-glycolylneuraminic acid-containing glycoforms at O126 and the relative abundance of glycoforms containing additional lactosamine units at N83 despite being considered very similar rhEPOs. These results demonstrated that the established methodologies show great potential for applications in quality control, biosimilar assessment, and the characterization of glycoprotein-based therapeutics.