Analysis & sensing最新文献

Structure-switching aptamers are utilized in various applications and have increasingly been translated into electrochemical biosensors, largely thanks to post-SELEX sequence engineering through computational and enzymatic approaches. In the context of sequence engineering, it is envisioned that folding and binding thermodynamics could likewise contribute to accelerating translation of aptamers into sensors. Herein, this is explored by first characterizing a series of quinine-binding aptamers using the biophysical methods isothermal titration calorimetry and nano differential scanning calorimetry. The folding and binding thermodynamics obtained are compared with the resulting analytical performance when aptamers are adapted into sensors. The findings show that the magnitude of sensor response is strongly correlated with aspects of the binding and unfolding thermodynamics of the aptamer as measured in solution. Using a similar approach, a recently reported adenosine monophosphate aptamer is successfully engineered to support electrochemical sensing. It is envisioned that relying on solution-based biophysical methods will further improve post-SELEX sequence engineering.

Laser-induced breakdown spectroscopy (LIBS) is a rapid, accurate technique for material analysis, offering real-time, minimally destructive, and in situ detection capabilities with broad application potential. LIBS extends its applications across various fields, from geology to biomedicine. However, barriers like matrix effects, reproducibility, self-absorption, and spectral noise often restrict the proper interpretation of the spectra. This review paper examines literature from 2015 to 2025, focusing on the evolution of machine learning (ML) and deep learning (DL) techniques, in LIBS analysis. It evaluates the advancement of these techniques, assessing both the qualitative and quantitative performance of LIBS analysis. These observations support the complementary roles of ML and DL methodologies. ML captures general patterns, while DL, through convolutional neural networks (CNNs), excels at identifying high-level features. This literature review reveals that no single ML or DL tool consistently provides optimal solutions for LIBS applications. The analysis pipeline needs to be tailored based on the LIBS data and the goal of the study. Designing such a framework requires the incorporation of preprocessing techniques to enhance the quality of raw signals. This step should then be followed by integrating the data into predictive models, whether ML or DL, to accomplish tasks like classification or concentration prediction.

An azobenzene-based tripodal sensor leverages the photoswitching property by undergoing reversible cis–trans photoisomerization where the two isomers of a sensor demonstrate differential binding affinities toward a series of biologically important nucleotides. This difference in binding behavior enables biologically important analytes, namely ATP, ADP, GTP, UMP, UTP, CTP, and inorganic phosphates to be detected and discriminated based on their interaction patterns with each isomeric form of the photoswitchable sensor, which can be reversibly tuned by light. The differential interactions of the two photoisomeric probes are clearly apparent based on their UV-vis spectral responses, followed by the multivariate analysis of the spectral data in two and three dimensions. These results showcase the potential of the azobenzene-based tripodal photoswitch that could double up as tunable molecular sensors upon photoisomerization, paving the way for their applications in biological sensing and analytical chemistry.

Spin-resolved photoluminescence approach is employed for the enantioselective detection of L- and D-cysteine using quantum dot-functionalized ferromagnetic electrodes. The L-cysteine-functionalized system exhibited a spin polarization P_s of 21.5% and a quenching efficiency (QE) of 35.6%, while the D-cysteine system showed 9.2% P_s and 21.0% QE. The detection limits are determined to be 1.86 nM for L-cysteine and 20 nM for D-cysteine. These results demonstrate the validation of the chiral-induced spin selectivity effect and also establish a sensitive, spin-based fluorescence platform for enantioselective biosensing of amino acids.

Linear unmixing spectral analysis is a technique where signals from tens of fluorophores can be deconvoluted to increase multiplexing by 4–5-fold. For the mathematical algorithm-driven analysis to be applied to analytical assays, there is a need to develop spectrally engineered nanoparticle probes. Herein, silica-encapsulated quantum dot (QD-SiO₂) nanoparticles with tunable spectral emissions are presented. The mechanism and factors for incorporating hydrophobic quantum dots (QDs) in silica in the reverse microemulsion synthesis are investigated, including ¹H NMR study on the interaction of ligands on QDs with the surfactant. The optimized synthesis reduces Förster resonance energy transfer between QDs in silica particles. In combination with linear unmixing analysis, nanoparticles that encapsulate varying ratios of different color QDs enable multiplexing capability up to 8. Their size of ca. 30 nm can enable in vitro imaging in addition to the use in existing immunoassays and analytical platforms.

In vitro cell density measuring studies are mostly based on colorimetric methods; however, these approaches are limited to endpoint measurements rather than continuous data. To obtain more sensitive and continuous data, real-time monitoring of cell density is essential, which requires the development of surfaces with optimal physicochemical properties. TiO₂ nanoporous structures are promising due to their favorable electrical properties, simple synthesis, unique porosity, biocompatibility, and stability. This study examined TiO₂ films deposited onto titanium plates and screen-printed electrodes, characterized by scanning electron microscopy (SEM) and electrochemical techniques. HeLa and A549 cell proliferation on TiO₂ was assessed and compared to polystyrene. Cell adhesion was evaluated via DAPI staining, fluorescence microscopy, and SEM. Electrochemical analyses (CV and EIS) were conducted on TiO₂-coated electrodes. Results showed comparable proliferation on TiO₂ and polystyrene, with effective adhesion confirmed by SEM. Electrochemical data demonstrated high sensitivity in detecting cellular differences, with detection limits of 150 cells for A549 and 107 for HeLa. These findings highlight TiO₂ nanoporous structures as promising candidates for cell-based biosensor platforms.

Early detection of Alzheimer's disease (AD) remains a critical challenge in neurodiagnostics that necessitates the development of noninvasive, cost-effective, and reliable biosensing platforms. Herein, a self-recognizing L-glutamine-conjugated manganese oxide (Gln-MnO₂)-modified electrode is developed for the electrochemical detection of L-glutamine (Gln) in a microdroplet of human saliva, a potential biomarker for early-stage AD. The biofunctionalized MnO₂ interface facilitates hydrogen bonding and electrostatic interactions with Gln, eliminating the need for enzymatic recognition elements while enhancing its sensitivity. Electrochemical characterization demonstrates a reversible, adsorption-controlled redox process with an electroactive surface area of 0.225 cm², high sensitivity (3.16 µA cm⁻² nM⁻¹), and a low detection limit (69.7 pM). Real saliva analysis confirms its practical utility, with measured Gln concentrations (6.70 ± 0.93 µM) aligning with reported physiological levels. Furthermore, standard addition experiments yield a recovery of 94.67 ± 5.14%, which also validates its accuracy. The Gln-MnO₂ sensor exhibits excellent operational stability over 15 days, with minimal signal loss afterward due to nonspecific adsorption in complex biofluids. The developed platform offers a cost-effective, enzyme-free, and disposable sensing approach for AD biomarker detection, with potential applications in metabolic monitoring and point-of-care diagnostics.

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.

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.