Analysis & sensing最新文献

Iodide sensing has a crucial role in clinical research and synthetic chemistry. However, its detection using small molecular receptors is not well explored compared to metal-organic framework systems, which are laborious to synthesize and characterize. Here, we report the facile synthesis and selective detection of iodide by a series of substituted benzoate-linked 4,6-O-benzylidene-N-glucosylamines exhibiting good gelation with aliphatic and aromatic solvents (0.5%w/v CGC in aromatic solvents) and gel-sol transition in the presence of iodide (0.3%w/v) without external interference. The self-assembly systems were characterized through FE-SEM, DSC, variable temperature ¹H NMR studies, and DFT calculations, where the driving force for gel formation was found to be H-bonding, π-π stacking, and van der Waals force of interactions. The solution phase iodide sensing was done using colorimetry, UV-Vis spectroscopy, and ¹H NMR titration, where the iodide–sugar interaction was found to be through CH···I⁻ and Hbonding, again supported by DFT calculations.

The existence of nanoplastics (NPLs) in the environment has received continued attention in recent years due to their potential toxicological impacts. Nevertheless, there is a significant shortage of comprehensive considerations for the detection of NPLs. Specifically, the material, particle size, surface chemistry, and adsorption of xenobiotics of NPLs all shape their transport, toxicity, and environmental fate. Accurate NPL analysis is therefore essential for risk assessment and environmental monitoring. Surface-enhanced Raman spectroscopy (SERS) and electrochemical (EC) sensing have made significant progress in the detection of plastic contaminants, especially NPLs, which rely on their high sensitivity, portability for real-time applications in the field, and fascinating cost-effectiveness. Unfortunately, the rational technological coupling of both of them (EC-SERS) to improve NPLs detection performance has not yet been considered. In this perspective, the potential of EC-SERS in the analysis of NPLs is elucidated, and the respective application strengths and advances of the two technologies are highlighted, as well as the opportunities and challenges of their coupling.

2D conjugated metal–organic frameworks (2D c-MOFs) have experienced a booming development over the past decade as innovative sensing materials for electroanalytical applications. These materials inherit the advantages of traditional MOFs, such as porosity and structural flexibility, which enable sufficient and adjustable interactions with analytes while overcoming conductivity limitations to facilitate electric signal transduction. This review provides an overview of recent advancements in 2D c-MOF-based electroanalytical systems. The various types of 2D c-MOF-based sensors and the optimization strategies employed to enhance their sensing performance are summarized. By highlighting the achievements of 2D c-MOFs in analyzing a wide range of analytes in both gaseous and liquid states, this review underscores the versatility and considerable potential of these materials. Finally, the perspectives on the challenges and opportunities that lie ahead for future research in this field are presented.

2D conjugated metal–organic frameworks (2D c-MOFs) have experienced a booming development over the past decade as innovative sensing materials for electroanalytical applications. These materials inherit the advantages of traditional MOFs, such as porosity and structural flexibility, which enable sufficient and adjustable interactions with analytes while overcoming conductivity limitations to facilitate electric signal transduction. This review provides an overview of recent advancements in 2D c-MOF-based electroanalytical systems. The various types of 2D c-MOF-based sensors and the optimization strategies employed to enhance their sensing performance are summarized. By highlighting the achievements of 2D c-MOFs in analyzing a wide range of analytes in both gaseous and liquid states, this review underscores the versatility and considerable potential of these materials. Finally, the perspectives on the challenges and opportunities that lie ahead for future research in this field are presented.

Though widely used for the accurate determination of host–guest binding constants, traditional fluorescence titrations are often too laborious for high-throughput (HT) or multivariable screening. Herein, it is demonstrated that standard microplate readers can approximate binding affinities across multiple supramolecular systems, different analytes, and stoichiometries, offering a scalable and time-efficient alternative to fluorimeters. Using diverse model systems, including a pH-responsive dye, a Zn²⁺-binding fluorophore (8-hydroxyquinoline-5-sulfonic acid) (1:2), a ratiometric anion-responsive terpyridine complex (ZnCl₂(BPh-tpy)) which binds PPi (3:1), and a neutral guest-binding system (cucurbit[7]uril with proflavine, 1:1), it is shown that plate-reader measurements yield binding constants that closely mirror those obtained from the fluorimeter. Moreover, the HT multi-well plate format enables the simultaneous acquisition of large datasets that support statistically robust chemometric analysis. In each case, Support Vector Machine regression models are trained to predict analyte concentration with high accuracy (prediction errors of 2.7–4.3%). Additionally, the methodology provides practical estimations of limits of detection and limits of quantification using the same plate data, further enhancing its utility. This platform preserves analytical rigor and introduces a path toward integrating statistical robustness and machine learning in sensor development, using instrumentation readily available in most research settings at a fraction of the cost.

Electrochemical sensors for hydrogen can be useful for development and optimization of water electrolyzers and fuel cells based on hydrogen evolution and oxidation reactions (HER and HOR). A nanometer-sized hydrogen sensor used as a tip in the scanning electrochemical microscope (SECM) can probe HER and HOR electrocatalysts and photocatalysts at the nanoscale. However, Pt tips and chemically modified nanoelectrodes previously employed for hydrogen sensing suffer from surface passivation, low amperometric signal, and stability issues. Here, the preparation of hydrogen sensors by covalently attaching ferrocene groups to the surface of Pt or carbon nanoelectrodes through oxidation of ferroceneacetate ions is reported. Mediated oxidation of hydrogen at surface-modified nanoelectrodes produces measurable and stable current suitable for amperometric measurements and SECM imaging.

A diaper-based, flexible, and miniature enzymatic biofuel cell (EBFC) is fabricated on carbon-coated conductive threads to detect glucose in the absence of a potentiostat. To construct the EBFC anode, 1,4-Naphthoquinone redox mediator is immobilized with glucose oxidase (GOx) on multi-walled carbon nanotubes. Ag/Ag₂O redox couple-based cathode is developed and integrated into BFC cathode due to its ability to perform even in a limited oxygen environment. Gold nanowires (AuNWs) are prepared and utilized for electrical wiring of GOx to the electrode surface. The EBFCs performance are found to be greatly enhanced (eight fold) in the presence of AuNWs with a maximum power density of 117 μW cm⁻² at an open circuit potential of 0.43 V. The EBFC shows linear increase in short-circuit currents when exposed to different glucose concentrations. This configuration enables precise glucose detection in the absence of a potentiostat. The results indicate that the sensor could detect a wide range of glucose (0.25–10 mM) in artificial urine and real human samples. The sensor exhibits remarkable selectivity toward glucose in the presence of common interferences. To validate the sensor's performance, urine and blood samples are collected from three diabetic and three healthy volunteers. The results show a good correlation between both measurements, with a Pearson correlation coefficient of 0.89, suggesting the efficiency of the smart diaper sensor for real-time urine analysis.

Electrochemical DNA-based biosensors have shown great potential in the rapid and highly accurate detection of Escherichia coli (E. coli), offering advantages over conventional microbial detection techniques. By leveraging hybrid nanointerfaces, these biosensors are emerging as a promising alternative for the rapid, accurate, and affordable detection of E. coli. These biosensors offer real-time pathogen monitoring with minimal sample preparation by utilizing the high sensitivity of electrochemical transduction and the specificity of DNA hybridization. This comprehensive review covers the basic principles of electrochemical DNA biosensor functioning, transduction mechanism-based classification, and different immobilization techniques to improve biosensing performance. Significant developments in signal amplification, nanomaterial integration, and electrode surface modifications-particularly through the design of hybrid nanointerfaces, are reviewed, showing enhancements in detection stability, sensitivity, and selectivity. Additionally, the integration of nanomaterials has greatly enhanced sensor performance by improving signal stability and reducing the detection time. Despite these developments, problems with sample complexity, sensor downsizing, and practical implementation still exist. This review aims to highlight the most recent advancements, potential commercialization applications, and future directions in the field to facilitate the development of next-generation biosensors for the detection of pathogens.

The clustered regularly interspaced short palindromic repeat (CRISPR)/Cas system, initially identified as a bacterial adaptive immune mechanism, has emerged as a revolutionary tool in genome editing and molecular diagnostics. This review highlights recent advancements in engineering CRISPR/Cas systems to improve specificity in nucleic acid detection, particularly in single-base differences analysis. Mainly focus on Cas protein engineering (e.g., structure-guided mutagenesis, directed evolution) and guide RNA (gRNA) optimization (e.g., mismatch introduction, chemical modifications). Integration of artificial intelligence tools, such as AlphaFold3 for structural prediction and machine learning for guide RNA design, may accelerate CRISPR/Cas system optimization. Despite progress, challenges persist in balancing specificity with efficiency and translating these technologies into clinical practice. By bridging computational innovation with experimental validation, CRISPR/Cas systems are poised to advance portable, scalable molecular diagnostics for precision medicine.

Early diagnosis of Alzheimer's disease (AD) is challenging due to the limitations of current biomarker detection methods. A customizable SERS-based aptasensor platform is presented that combines aptamer-functionalized gold nanoparticles (AuNPs) with machine learning (ML) algorithms for rapid and sensitive tau protein quantification, a key biomarker for AD. Through systematic evaluation of nanoprobes conjugated with four different tau-specific aptamer sequences, two aptamer configurations, named AT and BT, are identified as optimal candidates, demonstrating enhancement factors (EFs) of 2.12 × 10³ and 1.82 × 10³, respectively. This approach enables label-free detection within 30 min and integrates Random Forest (RF) and Convolutional Neural Network (CNN) models for concentration prediction of unknown samples. The RF models achieve remarkable accuracy with R² values of 0.998 for AT and 0.9999 for BT configurations, while the CNN models demonstrate strong performance with R² values of 0.968 (AT) and 0.986 (BT). The platform achieves a detection limit of 100 pM, well within the clinically relevant ranges. This label-free approach offers advantages in terms of rapid detection time, portability, and potential adaptability to other biomarkers. The integration of direct SERS sensing with ML algorithms for automated concentration prediction represents a promising advancement in biomarker analysis.