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The rapid and sensitive detection of trace organic pollutants in water is crucial for ensuring environmental safety. Traditional detection methods struggle to meet the demands of large-scale, real-time, and on-site detection. This paper reviews recent advances in the application of machine learning (ML) in spectral detection methods for trace organic pollutants. It introduces techniques such as data augmentation, intelligent feature extraction, and model construction, as well as their application in different spectral techniques, for example, generative adversarial networks (GANs) for data augmentation, convolutional neural networks (CNNs) for feature extraction, and random forests (RF) for classification and identification. It focuses on exploring the combination of different spectral techniques and ML methods, such as the antibiotic database established by combining surface-enhanced Raman spectroscopy (SERS) and CNNs, and the classification of microplastics using infrared spectroscopy combined with RF. Through these combinations, ML enhances the sensitivity, selectivity, and robustness of detection. Furthermore, it provides an in-depth analysis of model interpretability methods and cross-laboratory validation frameworks, emphasizing the importance of building standardized detection processes and evaluation systems. Looking ahead, research in this field will focus on more efficient ML algorithms, deep integration of hardware and algorithms, and the expansion of application scenarios, to build an AI-driven autonomous decision-making system for pollutant detection and treatment.

An innovative multi-color colorimetric method integrated with smartphone detection was designed with great sensitivity and specificity for the determination of mercury ions (Hg²⁺) in aqueous samples based on platinum nanoparticles (PtNPs) and gold nanorods (AuNRs). The peroxidase-mimicking activity of PtNPs triggered the conversion of 3,3′,5,5′-tetramethylbenzidine (TMB) into its oxidized form (TMB²⁺), and the resulting TMB²⁺ could quantitatively etch AuNRs, inducing a distinct color transition from blue-green to pale red. By forming metallophilic interactions with the PtNPs, Hg²⁺ effectively suppressed the etching process of the AuNRs by inhibiting PtNPs’ peroxidase-like catalytic activity. Based on this principle, a correlation allowed for the quantification of Hg²⁺ by measuring the average color intensity (I_c) calculated from red, green, and blue (RGB) values of the reaction solution, exhibiting a linear response ranging from 10 to 400 nM with a detection limit (LOD) of 5.1 nM. The method also exhibited outstanding selectivity and anti-interference capability against competing metal ions. Robust practical applicability was confirmed in complex real water samples with satisfactory spike-and-recovery results. Moreover, the proposed sensor integrated AuNRs served as a multi-color indicator for Hg²⁺-modulated peroxide-mimicking enzyme activity of PtNPs, with smartphone-based RGB analysis, presenting a rapid and cost-effective solution for field-based environmental monitoring of mercury contamination.

Correction for ‘High-sensitivity SpectroChip-integrated LFIA platform for rapid point-of-care quantification of cardiovascular biomarkers’ by Cheng-Hao Ko et al., Analyst, 2025, 150, 4930–4947, https://doi.org/10.1039/D5AN00846H.

The global rise of carbapenemase-producing Klebsiella pneumoniae (CPKP) poses a major threat to public health due to its high morbidity and mortality rates, driven largely by the production of carbapenemase enzymes. Rapid and accurate detection of carbapenemase activity is crucial for guiding appropriate antimicrobial therapy and controlling nosocomial transmission. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) enables direct detection of carbapenem hydrolysis but is currently limited by the drawbacks of conventional organic matrices, such as high background interference, toxic solvents, and poor reproducibility. In this study, we report a novel nanocomposite matrix, GNP@Zn–Cu/MOF@Au, synthesized via a green, aqueous-phase method without toxic reagents or extreme conditions. This heterostructured material exhibited a large mesoporous surface area, excellent UV absorption, and thermal responsiveness, which enhanced signal intensity, reproducibility, and analytical sensitivity, significantly improving MALDI-TOF MS performance. When applied to the identification of carbapenemase activity in clinical CPKP isolates, the matrix achieved 100% sensitivity and specificity after 1 hour of incubation and 97.3% sensitivity after just 15 minutes. Notably, KPC-2-producing strains were detected with full sensitivity within 15 minutes, while extended incubation enabled reliable identification of low-activity enzymes such as OXA-48. These findings demonstrate the strong potential of GNP@Zn–Cu/MOF@Au as a reliable and sustainable alternative to conventional matrices for rapid and accurate detection of carbapenemase activity in clinical diagnostics.

The persistent presence of 2,4-dichlorophenoxyacetic acid (2,4-D) in aquatic ecosystems poses a significant risk to both the environment and human health. This study presents a novel fluorescence-based sensor designed to detect ultra-trace amounts of the pesticide 2,4-D. The sensor utilizes a dual-functional approach by combining nitrogen-doped carbon dots (N-CDs) and molecularly imprinted polymers (MIPs) integrated into a PVDF membrane. N-CDs were synthesized using a hydrothermal method with citric acid and ethylenediamine (EDA). The N-CDs were then modified with 3-aminopropyltriethoxysilane (APTES) to enhance their photoluminescence stability and their ability to recognize specific molecules. These functionalized nanomaterials were then covalently attached to a Piranha-activated polyvinylidene fluoride (PVDF) membrane. For the first time, this modified membrane was used as a substrate for 2,4-D molecular imprinting and exploited to reveal its physicochemical resilience, high porosity, and modifiable surface chemistry. A Schiff-base sol–gel imprinting approach enabled the fabrication of a robust molecularly imprinted polymer (MIP) layer with high affinity and selectivity toward 2,4-D. The dual-functional N-CDs simultaneously served as optical transducers and recognition elements. Fluorescence quenching, specifically in the context of binding 2,4-D, was detected using smartphone imaging and ImageJ analysis. The proposed sensor exhibited satisfactory linearity in the 2,4-D concentration range of 10–150 μg L⁻¹ with the limit of detection (LOD) being 1.97 μg L⁻¹ and the limit of quantification (LOQ) being 5.97 μg L⁻¹. This sensor also exhibited photostability for 23 days at 4 °C, recoveries ranging from 96% to 103% and RSD value of less than 6. This work presents a smartphone-compatible MIP/N-CDs membrane designed for the detection of 2,4-D in water samples.

Liposome-based sensing technology that forms nanopores on membranes has attracted significant attention. We analyzed the characteristics of nanopore formation on liposomal membranes that result from interactions between amphotericin B (AmB) and ergosterol. Based on this property, we propose a new system that examines nanopore formation on liposomal membranes. We demonstrated the usefulness of this system by evaluating the effect of shodoamide C (ShC), which acts as a potentiator. Liposomes were prepared with a water-in-oil-in-water emulsion method and introduced into a microfluidic device through a power-free pumping method without the need for an external power supply. AmB binds to ergosterol in the lipid bilayer and creates nanopores that allow encapsulated molecules to escape. We controlled the release time by adjusting the mole fraction of ergosterol and the concentration of AmB. Fluorescence observation revealed that the release time depends on membrane composition and the concentration of AmB. We examined the effect of ShC, which enhances the activity of AmB, and found that this compound increases membrane permeability and accelerates molecular release. This study demonstrates the first analytical system that measures the activity of AmB and its enhancers or inhibitors through release profiles that depend on concentration. The system provides a practical tool for screening compounds that act on membranes. Our analytical system opens new opportunities for the development of membrane-active therapeutics and for progress in drug discovery and synthetic biology.

Bacterial infections remain among the leading causes of global mortality and represent significant challenges to public health. Conventional methods such as cultivation procedures, polymerase chain reaction, and instrumental techniques are routinely used in microbiology laboratories. However, these methods are time-consuming and labor-intensive, and require multi-step sample preparation. Moreover, there is a risk of sample contamination. Recently, vibrational spectroscopic techniques, including near-infrared, mid-infrared, and Raman spectroscopy, have gained considerable attention in microbiology research due to their high-throughput evaluation, non-destructive nature, rapid analysis, cost-effectiveness, and simplicity of application without the need for complex sample preparation steps. In combination with vibrational spectroscopy, chemometric analyses are employed to reduce data dimensionality, extract information related to the molecular structure of biological macromolecules, and eliminate irrelevant details. The present review discusses the applications of vibrational spectroscopy methods combined with chemometric approaches in various microbiological studies, including microbial viability assessment, bacterial inactivation evaluation, bacterial species identification, biofilm formation detection, and antibiotic resistance determination. Near-infrared, mid-infrared, and Raman spectroscopy techniques provide valuable information for clinical researchers and microbiologists within a short time by detecting key bacterial components such as proteins, nucleic acids, lipids, and polysaccharides across different spectral ranges. Despite their advantages, supplementary methods are still needed to enhance their precision. In the future, these techniques are expected to advance further to meet the needs of microbiological analyses more efficiently.

To support the transition toward a more circular economy, various technologies have been explored to convert plastic waste into valuable materials. Among these is pyrolysis, a thermochemical process that produces gas, solid, and liquid products, where the latter is known as plastic pyrolysis oil (PPO). These oils typically contain hydrocarbons and impurities such as oxygenates, nitrogenates, and organochlorides, depending on the composition of the plastic feedstock and process conditions. Since these contaminants must be removed before PPOs can be used in industrial steam crackers, their speciation is essential to define appropriate upgrading strategies. In this context, this study evaluates a gas chromatograph coupled with a halogen selective detector (GC-XSD) system, in combination with a comprehensive gas chromatograph system coupled with high resolution time-of-flight mass spectrometry (GC × GC-HR-TOFMS), for the identification of organochlorides in hydrocarbon matrices. Initially, the selectivity of the XSD, reactor temperature, and response to Cl standards were assessed. The method was then applied to the distilled fractions of a PPO. Using GC × GC-HR-TOFMS, several organochlorides were identified, including 1-chlorobutane, which was found at the highest concentration in the first distilled fraction (50–100 °C), and 3-(chloromethyl)heptane and 1,3-dichlorobezene, which were eluted primarily in the third fraction (150–200 °C). Other compounds, such as 1,2-dichloroethane, chlorobenzene, and 2-chloroethyl benzoate, were also detected at lower concentrations. The GC-XSD was proved to exhibit robust and selective capability for organochloride speciation in PPOs and could be potentially implemented in quality control and routine laboratories. When combined with hyphenated techniques such as GC × GC-HR-TOFMS, GC-XSD also facilitates the identification of unknown organochlorides.

Real-time monitoring of dopamine (DA) is vital for understanding neurophysiological processes and diagnosing neurological disorders. Flexible and stretchable sensors are particularly attractive for wearable or implantable bioelectronics, as they offer conformal contact with soft, dynamic biological tissues. However, achieving high selectivity in such platforms remains a major challenge, especially in the presence of structurally similar catecholamines such as epinephrine (EP), which often coexist with DA in physiological environments. Here, we report a highly stretchable hydrogel-based DA sensor constructed from acrylamide (AAM), carbon nanotubes (CNTs), and molybdenum disulfide (MoS₂). CNTs enhance the electrical conductivity of the hydrogel network, while MoS₂ provides selective affinity toward DA, enabling strong molecular discrimination against EP and common electroactive interferents such as ascorbic acid and uric acid. The resulting AAM/CNT/MoS₂ hydrogel exhibits excellent mechanical durability, maintaining structural integrity under 50% strain and surviving 15 repeated stretch-release cycles (0–50%) without loss of sensing performance. The sensor achieves a low detection limit of 6.1 nM and maintains reliable DA response even in the presence of high concentrations of EP. This work presents a promising strategy toward soft, selective, and interference-resilient biosensors for dynamic neurochemical sensing.

The simultaneous determination of the expression levels of multiple inflammation-associated cytokines in blood holds great promise for the early screening of cancer including colorectal cancer (CRC). Herein, an antibody microarray-based sandwich metal-enhanced immunofluorescent assay (AMSMEIFA) is developed for the quantitative measurement of five cytokines simultaneously through the fabrication of an antibody microarray on a slide coated with a poly(glycidyl methacrylate-co-2-hydroxyethyl methacrylate) layer and modified with gold nanorods (GNR@P(GMA-HEMA) slide). Benefiting from the metal-enhanced fluorescence (MEF) property and abundant antibody immobilization sites in the GNR@P(GMA-HEMA) slide, the newly developed AMSMEIFA enables the selective measurement of five pro-inflammatory soluble cytokines (interleukins (IL-1β, IL-2 and IL-6), tumor necrosis factor-α (TNF-α), and interferon-γ (IFN-γ)) with low limits of detection (LODs) at the sub-pg mL⁻¹ level. The practicability of AMSMEIFA is demonstrated by the simultaneous determination of five cytokines in 35 clinical serum samples, which are obtained from 25 CRC patients and 10 healthy donors (HDs). After analyzing the expression levels of five pro-inflammatory soluble cytokines using a machine learning (ML) model based on the least absolute shrinkage and selector operator (LASSO) regression, the area under the receiver operator characteristic curve (AUC) of CRC is as high as 0.92, demonstrating that ML-assisted AMSMEIFA could be used as a liquid biopsy for screening CRC with reasonable accuracy.