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In situ rapid detection of contaminants in environmental water is crucial for protecting the ecological environment and human health; however, it is always hindered by the complexity of sample matrices, trace content, and unknown species. Herein, we demonstrate a deep learning-based multicapturer surface-enhanced Raman scattering (SERS) platform on plasmonic nanocube metasurfaces for multiplex determination of organophosphorus pesticides (OPPs) residues. Poly(vinylpyrrolidone), 4-mercaptobenzoic acid, and l-cysteine are assembled on Ag nanocubes (AgNCs) and act as capturers to chemically define OPPs. Meanwhile, the OPPs-captured AgNCs efficiently close the interparticle distance and generate plasmonic metasurfaces, guaranteeing ultrasensitive and reproducible SERS analysis. Furthermore, by strategically combining all capturer-OPP SERS spectra, comprehensive "combined-SERS spectra" are reconstructed to enhance spectral variations of each OPP. Based on the combined-SERS spectra, a deep learning model is trained to predict OPPs, which significantly improve the qualitative and quantitative analysis accuracy. We successfully identified multiple OPPs in farmland, river, and fishpond water using this strategy. The whole detection procedure requires only 30 min, including sampling, SERS measurements, and deep learning analyses. This combination of a multicapturer SERS platform with the deep learning algorithm creates a rapid and reliable analytical strategy for multiplex detection of target molecules, providing a potential paradigm shift for environment-related research.

Identification of small-molecule binding sites in proteins is of great significance in analysis of protein function and drug design. Modified sites can be recognized via proteolytic cleavage followed by liquid chromatography-mass spectrometry (LC-MS); however, this has always been impeded by the complexity of peptide mixtures and the elaborate synthetic design for tags. Here, we demonstrate a novel technique for identifying protein binding sites using a deep learning-based label-free surface-enhanced Raman scattering (SERS) screening (DLSS) strategy. In DLSS, the deep learning model that was trained with large SERS signals could detect signal features of small molecules with high accuracy (99%). Without any secondary tag, the small molecules are directly complexed with proteins. After proteolysis and LC, SERS signals of all LC fractions are collected and input into the model, whereby the fractions containing the small-molecule-modified peptides can be recognized by the model and sent to MS/MS to identify the binding site(s). By using an automated DLSS system, we successfully identified the modification sites of fomepizole in alcohol dehydrogenase, which is coordinated with zinc along with three peptides. We also showed that the DLSS strategy works for identification of amino-acid residues that covalently bond with ibrutinib in Bruton tyrosine kinase. These results suggest that the DLSS strategy, which provides high molecular recognition capability to LC-MS analysis, has potential in drug discovery, proteomics, and metabolomics.

Dissolved organic matter (DOM) has been used frequently to distinguish different environmental samples based on its abundant fingerprint information. Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) is the most powerful technique to analyze the complex composition of DOM. Balancing between the reproducibility of peak magnitude and peak diversity is a key factor for achieving reliable and reproducible fingerprint information of DOM with FT-ICR-MS. In this paper, a novel magnitude filter (MGF) method and a novel MS-MGF strategy were proposed to improve the data reproducibility of FT-ICR-MS analysis. With the MS-MGF strategy, a 20% magnitude filter threshold (TMGF) was recommended to remove magnitude outliers, and a relatively low signal-to-noise ratio (SNR) threshold of 3.5 was recommended to retain those low but stable-magnitude peaks. The total relative magnitude was recommended since it could obtain better reproducibility of MS analysis compared to other types of peak magnitude. In addition, three replicates were enough to obtain satisfactory reproducibility. More importantly, the proposed MS-MGF strategy was also adaptable to different FT-ICR-MS instruments and different experimental conditions. Overall, the results are expected to initiate the promising applications of the MS-MGF strategy to distinguish the reliable fingerprint characteristics of DOM samples from different sources.

Since aggregation-induced electrochemiluminescence (AIECL) combined the merits of aggregation-induced emission (AIE) and electrochemiluminescence (ECL), it has become a research hotspot recently. Herein, novel kinds of functional metal-organic frameworks (MOFs) with strong AIECL were reported through doping tetraphenylethylene (TPE) into UiO-66. Due to the porosity and highly ordered topological structure that caused the confinement effect of MOFs, the molecular motion of TPE was effectively limited within UiO-66, resulting in strong AIE. Meanwhile, the large specific surface area and porous structure of UiO-66 allowed TPE to react with coreactants more effectively, which was beneficial to ECL. Thus, the TPE-functionalized UiO-66 (TPE-UiO-66) showed excellent AIECL performance surprisingly. Inspired by this, a multiple convertible ECL resonance energy transfer (ECL-RET) system was constructed through a DNA Y structure that regulated the distance between the energy donor (TPE-UiO-66) and different energy acceptors (gold nanoparticles and Adriamycin). Furthermore, an ultrasensitive ECL biosensor for the detection of Mucin 1 (MUC1) was developed through the introduction of the novel ECL-RET system. In the presence of MUC1, the DNA Y structure was constructed, keeping the gold nanoparticles (AuNPs) away from TPE-UiO-66. Then, Adriamycin (Dox) could be embedded in the DNA Y structure and act as an energy acceptor to receive the energy of TPE-UiO-66, which made the biosensor produce a strong ECL response. As expected, the developed ECL biosensor exhibited superior detection performance for MUC1. This work provided a novel way to realize AIECL and board the application of AIECL in analytical chemistry.

Based on the Venturi self-pumping effect, real-time sniffing with mass spectrometry (R-sniffing MS) is developed as a tool for direct and real-time mass spectrometric analysis of both gaseous and solid samples. It is capable of dual-mode operation in either gaseous or solid phase, with the corresponding techniques termed as R

Rapid component separation and reliable surface-enhanced Raman scattering (SERS) detection of organochlorine pesticide (OCP) residues in real water samples remain major challenges because of sample complexity, trace content, and low molecular affinity for a metal surface. Here, we report a novel strategy of simultaneous in situ extraction and fabrication of plasmonic colloidal gold superparticles (AuSPs) to perform rapid SERS detection of OCPs in environmental water. In this protocol, multiple components of OCPs in complex water were facilely diffused into dichloromethane (DCM) microdroplets and specifically bound to octadecylamine-modified gold nanoparticles (Au-ODAs), affording the SERS substrate through self-assembly of the OCP-trapped Au-ODA into AuSPs with the evaporation of DCM. SERS signals of the specifically prepared Au-ODA could be used as an internal standard to calibrate the absolute signal of OCPs, and multiplex detection could be achieved depending on their molecularly narrow Raman peaks. As for simultaneously sensing four kinds of OCPs (4,4'-DDT, α-endosulfan, tetradifon, and chlordane) in water, the established method showed strong anti-interference ability and comparable quantification ranges with a low limit of detection (LOD). The recoveries ranged from 90.20 to 109.4% for OCPs in farmland, river, and fishpond water, indicating that the established AuSP-based platform is reliable and applicable to the detection of OCPs in real water samples.

Noble metal nanoparticles (NPs) have enabled surface-enhanced Raman scattering (SERS) for in situ monitoring of NPs-catalyzed reactions. However, it still remains a great challenge to ensure that analytes without plasmonic metal surface-affinity groups (such as thiol and amino groups) can be located into hotspots and detected by SERS. Here, we report a novel sacrificial template method for the fabrication of "pomegranate-like" plasmonic nanoreactors (PPNs), in which high-density embedded AuNPs simultaneously generated SERS enhancement and catalytic performance. Once the analytes entering PPNs are catalyzed and meanwhile located into the hotspots, in situ SERS monitoring of catalytic reactions can be achieved. The intense hotspots of localized electric fields of PPNs were evaluated by finite-difference time-domain simulation. By using PPNs as a substrate, SERS signals of molecules without Au surface-affinity groups were obtained, such as

Programmed cell death protein 1 (PD-1) is one of the coinhibitory checkpoints upon T cell activation, the abnormal expression of which severely threatens host immune modulatation for chronic infection. Thus, fast and sensitive monitoring of PD-1 is of vital importance for early diagnosis and cancer treatment. The current detection methods largely based on enzyme-linked immunosorbent assay (ELISA) require time-consuming incubation and complicated washing steps. Herein, we designed a simple and portable nanofiber paper (NFP)-based fluorescence "off-on" immunosensor for PD-1 rapid determination. Molybdenum disulfide (MoS

Rapid component separation and reliable surface-enhanced Raman scattering (SERS) detection of organochlorine pesticide (OCP) residues in real water samples remain major challenges because of sample complexity, trace content, and low molecular affinity for a metal surface. Here, we report a novel strategy of simultaneous

Colorimetric analytical strategies exhibit great promise in developing on-site detection methods for antibiotics, while substantial recent research efforts remain problematic due to dissatisfactory sensitivity. Taking this into account, we develop a novel colorimetric sensor for in-field detection of antibiotics by using aptamer (Apt)-capped and horseradish peroxidise (HRP)-embedded zeolitic metal azolate framework-7 (MAF-7) (Apt/HRP@MAF-7) as target recognition and signal transduction, respectively. With the substrate 3,3',5,5'-tetramethylbenzidine (TMB)-impregnated chip attached on the lid, the assay can be conveniently operated in a tube and reliably quantified by a handheld colorimeter. Hydrophilic MAF-7 can not only prevent HRP aggregation but also enhance HRP activity, which would benefit its detection sensitivity. Besides, the catalytic activity of HRP@MAF-7 can be sealed through assembling with Apt and controllably released based on the bioresponsivity via forming target-Apt complexes. Consequently, a significant color signal can be observed owing to the oxidation of colorless TMB to its blue-green oxidized form oxTMB. As a proof-of-concept, portable detection of streptomycin was favorably achieved with excellent sensitivity, which is superior to most reported methods and commercial kits. The developed strategy affords a new design pattern for developing on-site antibiotics assays and immensely extends the application of enzyme embedded metal-organic framework composites.

We report a novel strategy of simultaneous in situ extraction and fabrication of surface-enhanced Raman scattering substrate (IE-SERS) to perform selective and reliable on-site determination of thiram residue in soil, fruits, and vegetables. In this protocol, the thiram residue on complex surfaces can facilely diffuse into the solvent (dichloromethane (DCM)) and specifically bind to gold nanoparticles (AuNPs), affording the SERS substrate through the embedding of the thiram-trapped AuNPs into the cellulose p-toluenesulfonates (CTSAs) film through the evaporation of DCM. SERS signals of the specifically prepared CTSAs could be used as an internal standard to calibrate the absolute signal of thiram, which can avoid the fluctuation of SERS intensities caused by uneven and irregular morphology of SERS substrate. Thus, reliable quantitation of thiram through SERS detection and superior reproducibility in the SERS measurement (RSD = 4.21%) were achieved. As for directly sensing the thiram residue in soil, the established method shows strong anti-interference ability and a good linear response from 0.1 to 12 μg/g with a low limit of detection (LOD) of 50 ng/g, which is lower than that of all the previously reported methods. The recoveries range from 91.76 to 112.3% for thiram in paddy soils, indicating that the established IE-SERS method is reliable and applicable to the detection of thiram residue in real soil samples. In addition, the measurement of the residual thiram on strawberry and cucumber surface was also successfully accomplished by this strategy, indicating that the established method also has great potential in the in situ ultrasensitive detection of thiram on irregular fruits and vegetables.

Differences in electrical conductivity provide a basis for identification of different components present at a material surface. Here it is demonstrated that this effect can be exploited for surface analysis of compound materials in solution by making localized alternating current impedance measurements.