Hydrogen Bonding-Induced Aggregation of Chiral Functionalized AuNS@Ag NPs for Photothermal Enantioanalysis.

Toward the challenges of signaling transduction amplified in enantioselective recognition, we herein devised an innovative strategy for highly selective recognition of amino acids and their derivatives, leveraging photothermal effects. In this approach, bifunctional l-ascorbic acid is employed to reduce silver ions in situ on Au nanostars. Simultaneously, its oxidate (l-dehydroascorbic acid) is bonded to the silver shell as a chiral selector to prepare chiral nanoparticles (C-AuNS@Ag NPs) with the ability to recognize stereoisomers and sensitively modulate the photothermal effect. l-Dehydroascorbic acid can selectively capture one of the enantiomers of the two forms through hydrogen bonding and drive aggregation of the nanoparticles, which sharply enhances the photothermal effect. Consequently, the two forms of the system exhibit a significant temperature difference, which enables the discrimination and quantification of enantiomers. Our strategy verifies that six chiral amino acids and their derivatives can be discriminated with enantioselective response values of up to 79. Additionally, the chiral recognition mechanism was revealed through density functional theory (DFT) calculations, providing a paradigm shift in the development of enantiomeric recognition strategies.

Intra-nanoparticle plasmonic nanogap based spatial-confinement SERS analysis of polypeptides.

Sensing and characterizing water-soluble polypeptides are essential in various biological applications. However, detecting polypeptides using Surface-Enhanced Raman Scattering (SERS) remains a challenge due to the dominance of aromatic amino acid residues and backbones in the signal, which hinders the detection of non-aromatic amino acid residues. Herein, intra-nanoparticle plasmonic nanogap were designed by etching the Ag shell in Au@AgNPs (i.e., obtaining AuAg cores) with chlorauric acid under mild conditions, at the same time forming the outermost Au shell and the void between the AuAg cores and the Au shell (AuAg@void@Au). By varying the Ag to added chloroauric acid molar ratios, we pioneered a simple, controllable, and general synthetic strategy to form interlayer-free nanoparticles with tunable Au shell thickness, achieving precise regulation of electric field enhancement within the intra-nanogap. As validation, two polypeptide molecules, bacitracin and insulin B, were successfully synchronously encapsulated and spatial-confined in the intra-nanogap for sensing. Compared with concentrated 50 nm AuNPs and Au@AgNPs as SERS substrates, our simultaneous detection method improved the sensitivity of the assay while benefiting to obtain more comprehensive characteristic peaks of polypeptides. The synthetic strategy of confining analytes while fabricating plasmonic nanostructures enables the diffusion of target molecules into the nanogap in a highly specific and sensitive manner, providing the majority of the functionality required to achieve peptide detection or sequencing without the hassle of labeling.

A novel laccase-like Cu-MOF for colorimetric differentiation and detection of phenolic compounds.

The development of convenient, fast, and cost-effective methods for differentiating and detecting common organic pollutant phenols has become increasingly important for environmental and food safety. In this study, a copper metal-organic framework (Cu-MOF) with flower-like morphology was synthesized using 2-methylimidazole (2-MI) as ligands. The Cu-MOF was designed to mimic the natural laccase active site and proved demonstrated excellent mimicry of enzyme-like activity. Leveraging the superior properties of the constructed Cu-MOF, a colorimetric method was developed for analyzing phenolic compounds. This method exhibited a wide linear range from 0.1 to 100 µM with a low limit of detection (LOD) of 0.068 µM. Besides, by employing principal component analysis (PCA), nine kinds of phenols was successfully distinguished and identified. Moreover, the combination of smartphones with RGB profiling enabled real-time, quantitative, and high-throughput detection of phenols. Therefore, this work presents a paradigm and offers guidance for the differentiation and detection of phenolic pollutants in the environment.

Dual-ligand lanthanide metal-organic framework probe for ratiometric fluorescence detection of mercury ions in wastewater.

By serving dipyridylic acid (DPA) and 2,5-dihydroxyterephthalic acid (DHTA) as the biligands, a novel lanthanide (Eu3+) metal-organic framework (MOF) namely Eu-DHTA/DPA was prepared for specific Hg2+ fluorescence determination. The dual-ligand approach can endows the resulting luminescent MOF with dual emission of ratiometric fluorescence and uniform size. Eu3+ produces intense red fluorescence when activated by the ligand DPA, while the other ligand DHTA produces yellow fluorescence. Under 273 nm excitation, the presence of Hg2+ in the monitoring environment causes an increase in the intensity of the DHTA fluorescence peak at 559 nm and a decrease in the intensity of the Eu3+ fluorescence peak at 616 nm. Hg2+ effectively quenches the fluorescence emission of the central metal Eu3+ in Eu-DHTA/DPA at 616 nm through a dynamic quenching effect. This recognition process occurs due to the coordination of Hg2+ with ligands such as benzene rings, carboxyl groups, and pyridine N in three-dimensional space. Hg2+ was detected by measuring the ratio between two fluorescence peaks (I559 nm/I616 nm) within the range 2-20 µM, achieving a remarkably low detection limit of 40 nM. The established ratiometric fluorescence method has been successfully applied to the determination of Hg2+ in industrial wastewater of complex composition. The method plays a crucial role in the rapid and sensitive monitoring of Hg2+ in real environmental samples. The recoveries ranged from 92.82% to 112.67% (n = 3) with relative standard deviations (RSD) below 4.8%. This study offers a convenient and effective method for constructing probes for Hg2+ monitoring, with practical applications in environmental monitoring.

Machine learning-driven 3D plasmonic cellulose sensor for in situ rapid SERS detection of bisphenol compounds in water sample.

Rapid component separation and accurate identification of bisphenols compounds (BPs) in real water sample remain an attractive challenge due to the trace amounts and structural similarities of BPs, and complexity of real samples. Here, we designed and synthesized chemically modified cellulose p-toluenesulfonate (CTSA) to encapsulate octadecylamine-modified gold nanoparticles (Au-ODA), obtaining 3D plasmonic cellulose (Au@CTSA). Simultaneously, by virtue of the high surface area in the 3D network of CTSA and the solvent volatile deposition, BPs in water were in situ extracted and concentrated in Au@CTSA microspheres. Since the 3D network of Au@CTSA supports the formation of "hotspots", the number of "hotspots" available is greatly improved, enabling excellent SERS detection of BPs. Based on the collected SERS spectra, machine learning was utilized to analyze the overall profile of BPs, which eliminated the subjective judgment of the concentration by the Au@CTSA sensor using a single characteristic peak. In this way, the accuracy of identification of BPs was significantly improved. The machine learning-driven Au@CTSA sensor realized the detection of traces bisphenol A (BPA), bisphenol S (BPS), and bisphenol F (BPF) in water sample, pushing quantitative detection of different concentrations of BPs and contributing facile indicators for water quality monitoring.

Quinine-Fabricated Surface-Enhanced Raman Spectroscopy Chiral Sensing Platform Enables Simultaneous Enantioselective Discrimination and Identification of Aliphatic Amino Acids.

Due to low optical activity and structural simplicity, synchronous chiral discrimination and identification of aliphatic amino acids (AAs) are still challenging yet demanding. Herein, we developed a novel surface-enhanced Raman spectroscopy (SERS)-based chiral discrimination-sensing platform for aliphatic AAs, in which l- and d-enantiomers are able to discriminately bind with quinine to generate distinct differences in the SERS vibrational modes. Meanwhile, the plasmonic sub-nanometer gaps supported by the rigid quinine enable the maximization of SERS signal enhancement to reveal feeble signals, allowing for simultaneously acquiring the structural specificity and enantioselectivity of aliphatic amino acid enantiomers in a single SERS spectrum. Different kinds of chiral aliphatic AAs were successfully identified by using this sensing platform, demonstrating its potential and practicality in recognizing chiral aliphatic molecules.

Deep Learning-Based Multicapturer SERS Platform on Plasmonic Nanocube Metasurfaces for Multiplex Detection of Organophosphorus Pesticides in Environmental Water.

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.