Analytical Chemistry最新文献

Cyclic voltammetry (CV) is a standard method for assessing electrochemical properties in the electrochemical cells, typically in conventional aqueous contexts like 1 m solutions ("salt-in-water"). However, recent advancements have extended electrochemistry into superconcentrated regimes, such as "water-in-salt" solutions with concentrations above 10 to 20 m, which require large amounts of salt for experiments. To address this, machine learning (ML) has been applied, coupled with in-house data collection using lithium bis(trifluoromethanesulfonyl)imide (LiTFSI) electrolytes. This work demonstrates the electrochemistry of YEC-8B in LiTFSI, given their broad potential window of up to 3.0 V across concentrations from 1 to 20 m. The CV profiles were divided into two models: the upper curve for charging and the lower curve for discharging. Data were normalized and segmented by percentiles, and a decision tree model was developed to predict outputs based on input parameters like LiTFSI concentration, scan rates, and potential window. The model predicted nine target variables with a mean absolute percentage error of approximately 2% for both the upper and the lower CV profile curves. Trapezoidal rule was then used to calculate the system's capacitance. Additionally, tests showed a 75% accuracy in predicting the potential window and a suitable scan rate. Overall, the model effectively demonstrated the relationship between "water-in-salt" electrolytes and CV profiles in an electrochemical context using a simple machine learning (ML) algorithm, which continues to expand the integration of data science and electrochemistry.

As a highly aggressive malignancy, the issue of curing melanoma at an advanced stage could suffer from severe metastasis and a lower 5-year survival rate. Therefore, the early diagnosis of melanoma with high accuracy is vital and contributes to a significantly improved 5-year survival rate. This work reports a dual-locked receptor, m-BA-Hcy, which releases the near-infrared (NIR) fluorophore Hcy-OH upon the dual activation of reactive oxygen species (ROS) and tyrosinase (TYR). The substitution of boric acid on the phenyl ring was studied, which influences the feasibility of the performance of the envisaged cascade reaction. The sensing behavior was discussed in terms of optical spectroscopy and reaction mechanism, and imaging was fully performed at the cellular and organism levels. Receptor m-BA-Hcy was hence clarified to possess supreme sensitivity and accuracy for melanoma detection.

mRNA, a critical biomarker for various diseases and a promising target for cancer therapy, is central to biological and medical research. However, the development of multiplexed approaches for in situ monitoring of mRNA in live cells are limited by their reliance on enzyme-based signal amplification, challenges with in situ signal diffusion, and the complexity of nucleic acid design. In this study, we introduce a nonenzymatic catalytic DNA assembly (NEDA) technique to address these limitations. NEDA facilitates the precise in situ imaging of intracellular mRNA by assembling three free hairpin DNA amplifiers into a low-mobility, three-dimensional DNA spherical structure. This approach also enables the simultaneous detection of four distinct targets via the combination of fluorescent signals, with a detection limit as low as 141.2 pM for target mRNA. To enhance the efficiency of nucleic acid design, we employed computer-aided design (CAD) to rapidly generate feasible sequences for highly multiplexed detection. By integrating various machine learning algorithms, we achieved impressive accuracy of nearly 96.66% in distinguishing multiple cell types and 87.80% in identifying the same cell type under different drug stimulation conditions. Notably, our platform can also identify drug stimuli with similar mechanisms of action, highlighting its potential in drug development. This multiplexed 3D assembly sensing strategy with CAD not only enhances the ability to image nucleic acid sequences in situ simultaneously but also provides a novel platform for efficient molecular diagnostics and personalized therapy.

The pore microstructure of mesoporous materials has a vital influence on molecular movement and assembly as well as crystallization. Nonetheless, previous studies have predominantly concentrated on the impact of pore size and pore shape on molecular assembly and nucleation outcomes; investigations delving into the effects of more complex pore structures on molecular assembly and nucleation behaviors were absent. In this study, evolution of the molecular self-assembly process of flufenamic acid (FFA) confined in mesoporous materials with different microstructures was monitored by in situ ¹⁹F solid-state NMR spectroscopy. It was demonstrated that tortuosity, as a microstructural parameter of porous materials, has the ability to determine the molecular assembly process and nucleation behaviors of FFA. The results indicated that molecules in pores with high tortuosity tend to aggregate to an amorphous plug, while those in less tortuous nanopores are inclined to adsorb on the pore surface forming molecular layers. Besides that, this work provides the first direct proof that a mixture of two molecular layer structures exists on the FFA-silica surface through ¹⁹F solid-state NMR spectroscopy. This study explores the relationship between the microstructure of porous materials and molecular assembly, which can inform drug delivery, electronic deposition, and biomineralization.

Affinity selection-mass spectrometry (AS-MS) is a ligand discovery platform that relies upon mass spectrometry to identify molecules bound to a biomolecular target. When utilized with large peptide libraries (10⁸ members), AS-MS sample complexity can surpass the sequencing capacity of modern mass spectrometers, resulting in incomplete data, identification of few target-specific ligands, and/or incomplete sequencing. To address this challenge, we introduce pyBinder to perform quantitation on AS-MS data to process primary MS¹ data and develop two scores to rank the peptides from the integration of their peak area: target selectivity and concentration-dependent enrichment. We benchmark pyBinder utilizing AS-MS data developed against antihemagglutinin antibody 12ca5, revealing that peptides that contain a motif known for target-specific high-affinity binding are well characterized by these two scores. AS-MS data from a second protein target, WD Repeat Domain 5 (WDR5), is analyzed to confirm the two pyBinder scores reliably capture the target-specific motif-containing peptides. From the results delivered by pyBinder, a list of target-selective features is developed and fed back into subsequent MS experiments to facilitate expanded data generation and the targeted discovery of selective ligands. pyBinder analysis resulted in a 4-fold increase in motif-containing sequence identification for WDR5 (from 3 to 14 ligands discovered), showing the utility of the two scores. This work establishes an improved approach for AS-MS to enable discovery outcomes (i.e., more ligands identified), but also a way to compare AS-MS data across samples, protocols, and conditions broadly.