Controlled Synthesis of Preferential Facet-Exposed Fe-MOFs for Ultrasensitive Detection of Peroxides.

Exposing different facets on metal-organic frameworks (MOFs) is highly desirable to enhance the performance for various applications, however, exploiting a concise and effective approach to achieve facet-controlled synthesis of MOFs remains challenging. Here, by modulating the ratio of metal precursors to ligands, the facet-engineered iron-based MOFs (Fe-MOFs) exhibits enhanced catalytic activity for Fenton reaction are explored, and the mechanism of facet-dependent performance is revealed in detail. Fully exposed (101) and (100) facets on spindle-shaped Fe-MOFs enable rapid oxidation of colorless o-phenylenediamine (OPD) to colored products, thereby establishing a dual-mode platform for the detection of hydrogen peroxide (H2O2) and triacetone triperoxide (TATP). Thus, a detection limit as low as 2.06 nm is achieved, and robust selectivity against a wide range of common substances (>16 types) is obtained, which is further improved by incorporating a deep learning architecture with an SE-VGG16 network model, enabling precise differentiation of oxidizing agents from captured images. The present strategy is expected will shine light on both the rational synthesis of nanomaterials with modulated morphologies and the exploitation of high-performance trace chemical sensors.

Electrophilicity Modulation for Sub-ppm Visualization and Discrimination of EDA.

Precise and timely recognition of hazardous chemical substances is of great significance for safeguarding human health, ecological environment, public security, etc., especially crucial for adopting appropriate disposition measures. Up to now, there remains a practical challenge to sensitively detect and differentiate organic amines with similar chemical structures with intuitive analysis outcomes. Here, a unique optical probe with two electrophilic recognition sites for rapid and ultra-sensitive ratiometric fluorescence detection of ethylenediamine (EDA) is presented, while producing distinct fluorescence signals to its structural analog. The probe exhibits ppb/nmol level sensitivity to liquidous and gaseous EDA, specific recognition toward EDA without disturbance to up to 28 potential interferents, as well as rapid fluorescence response within 0.2 s. By further combining the portable sensing chip with the convolutional algorithm endowed with image processing, this work cracked the problem of precisely discriminating the target and non-targets at extremely low concentrations.

Functionalized magnetic nanobeads for SERS-based detection of Staphylococcus aureus.

In this study, we introduced a Raman detection technique based on a combination of functionalized magnetic beads and surface-enhanced Raman scattering (SERS) tags to develop a rapid and sensitive strategy for the detection of Staphylococcus aureus (S. aureus), a typical foodborne pathogen. Polyethylene glycol (PEG) and bovine serum albumin (BSA) dual-mediated teicoplanin functionalized magnetic beads (TEI-BPBs) were prepared for separation of target bacteria. SERS tags were used to immobilize antibodies on gold surfaces with bifunctional linker proteins to ensure specific recognition of S. aureus. Under optimal conditions, the combination of TEI-BPBs and SERS tags showed reliable performance, exhibiting good capture efficiency even in the presence of 106 CFU mL-1 of non-target bacteria. The SERS tag provided an effective hot spot for subsequent Raman detection, presenting good linearity in the range of 102-107 CFU mL-1. Good performance has also been shown in detecting target bacteria in milk samples, where it has a recovery of 95.5-101.3%. Thus, the highly sensitive Raman detection technique combined with TEI-BPBs capture probes and SERS tags is a promising method for the detection of foodborne pathogens in food or clinical samples.

Amplification-free detection of HBV DNA mediated by CRISPR-Cas12a using surface-enhanced Raman spectroscopy.

Nucleic acid markers have been widely used in the detection of various virus-related diseases, including hepatitis B virus (HBV), which is spreading worldwide. The trans-activated CRISPR-Cas system has shown excellent sensitivity and specificity in nucleic acid detection. However, nucleic acid testing usually requires amplification of the target nucleic acid for more accurate and specific detection; furthermore, current nucleic acid assays are time-consuming, costly, and are limited by non-specific cross-reactivity. We developed an amplification-free viral DNA biosensor-based diagnostic method that uses a clustered regularly interspaced short palindromic repeats-associated system (CRISPR/Cas)-based approach with surface enhanced Raman spectroscopy. This method can specifically identify the target site by changing the crRNA sequence. In addition, the incubation period and development of the disease can be determined by quantitative detection of viral DNA. This system could achieve rapid and highly sensitive detection of HBV DNA within 50 min and vast detection range from 0.1 pM to 1 nM. Therefore, a combined CRISPR/Cas12a-SERS-based assay would improve the sensitivity of detection in assays using multiple biomarkers. In conclusion, our CRISPR/Cas12a-based biosensor would enable rapid, simple, and sensitive detection of HBV nucleic acids.

CRISPR/dCas9-surface-enhanced Raman scattering for the detection of drug resistance gene macB.

Antibiotics have brought many benefits to public health systems worldwide since their first use in the last century, yet with their overuse in clinical treatment and livestock farming, new public health issues have arisen. Previously, we found in our experiments that the levels of macB genes in bovine raw milk ranked among the top of many drug resistance genes. In this paper, we present an analysis of regularly interspaced clustered short palindromic repeats (CRISPR) combined with surface-enhanced Raman scattering (SERS) technology for the detection of the drug resistance gene macB. The analysis was accomplished through the collaboration of the CRISPR system's ability to specifically identify genes and the more sensitive performance of the SERS. The analysis detects the drug resistance gene macB and does not yet require complex steps such as nucleic acid amplification. This method may prove to be an effective method for accurate detection of the drug-resistant gene macB, thus enabling more effective prevention of contamination of drug-resistant genes in food hygiene.

Raman spectroscopy-based adversarial network combined with SVM for detection of foodborne pathogenic bacteria.

Raman spectroscopy combined with artificial intelligence algorithms have been widely explored and focused on in recent years for food safety testing. It is still a challenge to overcome the cumbersome culture process of bacteria and the need for a large number of samples, which hinder qualitative analysis, to obtain a high classification accuracy. In this paper, we propose a method based on Raman spectroscopy combined with generative adversarial network and multiclass support vector machine to classify foodborne pathogenic bacteria. 30,000 iterations of generative adversarial network are trained for three strains of bacteria, generative model G generates data similar to the actual samples, discriminant model D verifies the accuracy of the generated data, and 19 feature variables are obtained by selecting the feature bands according to the Raman spectroscopy pattern. Better classification results are obtained by optimising the parameters of the multi-class support vector machine, etc. Our detection and classification method not only solves the problem of needing a large number of samples as training set, but also improves the accuracy of the classification model. Therefore, this GAN-SVM classification model provides a new idea for the detection of bacteria based on Raman spectroscopy technology combined with artificial intelligence algorithms.