Polyethyleneimine (PEI)-functionalized nanochannels have been extensively exploited in ion gating and biosensing. It is of great significance to explore their conductance determining ion-surface interaction and ionic transport at the nanoscale. Here, pH-regulated conductance of PEI-coated nanochannels is theoretically studied. The results suggest that coating PEI may improve nanochannel conductance in different ways depending on the surface charge of nanochannels. The conductance of a PEI-coated PET nanochannel whose surface charge is assumed to be constant for simplicity is weakly changed and then decreased to that of a non-PEI-coated PET nanochannel with increasing pH, which is consistent with the reported experimental results. The conductance of a PEI-coated silica nanochannel, whose surface charge largely depends on pH, ion concentration, and nanochannel radius, is significantly increased and, subsequently, decreased to that of a non-PEI-coated silica nanochannel as pH rises, which is rarely reported in experiments. This work provides a fundamental framework to investigate the conductance of PEI-coated nanochannels, and the PEI-coated silica nanochannels with unique pH-dependent conductance may be explored in the construction of mesoporous silica thin films for biomedical analytical applications.

Electrochemiluminescence (ECL) produces light through electrochemical reactions and has shown promise for various analytic applications in biomedicine. However, the use of ECL devices (ECLDs) as light sources has been limited due to insufficient light output and low operational stability. In this study, we present a high-power pulsed operation strategy for ECLDs to address these limitations and demonstrate their effectiveness in optogenetic manipulation. By applying a biphasic voltage sequence with short opposing phases, we achieve intense and efficient ECL through an exciplex-formation reaction pathway. This approach results in an exceptionally high optical power density, exceeding 100 μW mm-², for several thousand pulses. Balancing the ion concentration by optimizing the voltage waveform further improves device stability. By incorporating multiple optimized pulses into a pulse train separated by short rest periods, extended light pulses of high brightness and with minimal power loss over time were obtained. These strategies were leveraged to elicit a robust optogenetic response in fruit fly (Drosophila melanogaster) larvae expressing the optogenetic effector CsChrimson. The semi-transparent nature of ECLDs facilitates simultaneous imaging of larval behaviour from underneath, through the device. These findings highlight the potential of ECLDs as versatile optical tools in biomedical and neurophotonics research.

Hepatocellular carcinoma (HCC) is one of the most lethal malignancies worldwide, and early diagnosis is crucial. Golgi protein 73 (GP73) has emerged as a promising serum biomarker for HCC. However, current detection methods often fail to meet routine screening requirements due to limitations in sensitivity and operational simplicity. To address these challenges, we have developed a novel fluorescent aptamer-based sensor for highly sensitive GP73 detection based on the fluorescence resonance energy transfer (FRET) mechanism between graphitic carbon nitride quantum dots (g-CNQDs) and a copper-based metal-organic framework (Cu-TCPP). g-CNQDs were covalently conjugated with a GP73-specific aptamer to serve as the fluorescent donor, while two-dimensional Cu-TCPP nanosheets acted as the efficient acceptors. Fluorescence was quenched upon donor-acceptor interaction via FRET. In the presence of GP73, aptamer-target binding disrupted FRET interaction, separating the donor from the acceptor and restoring fluorescence in a concentration-dependent manner. Under optimal conditions, the sensor exhibited excellent linearity over a concentration range 1.0-225.0 ng mL⁻¹, with a detection limit as low as 0.907 ng mL⁻¹. Recoveries for spiked human serum samples ranged from 95.96% to 103.85%, with relative standard deviations (RSDs) of 0.38%-5.48%. The developed aptamer sensor demonstrated excellent sensitivity, selectivity, and stability, providing a powerful tool for early HCC diagnosis and offering strong potential for real-time analytical applications.

Protein aggregation poses a significant risk to biopharmaceutical product quality, as even minor amounts of oligomeric species can compromise efficacy and safety. Rapid and reliable detection of protein aggregates thus remains a major challenge in biopharmaceutical manufacturing. Although traditional offline methods such as size-exclusion chromatography provide accurate results, their inherent time delays limit real-time process control capabilities. Consequently, there is an urgent scientific need for inline analytical techniques capable of selectively quantifying protein monomers and aggregates in real time to facilitate immediate corrective actions and enhance overall process robustness. Raman spectroscopy, as a tool for a process analytical technology application, is especially suitable due to its molecular specificity, rapid data acquisition, and compatibility with aqueous solutions commonly used in biopharmaceutical manufacturing. Addressing this need, this study establishes a Raman spectroscopy-based strategy for the selective detection and quantification of monomeric and aggregated forms of a model protein (bovine serum albumin). Controlled stress conditions were applied to generate aggregated species reproducibly, and a Latin Hypercube sampling design was used to independently vary protein concentration and aggregate fraction, ensuring that observed spectral effects were attributable to aggregation rather than concentration differences. Furthermore, spectral markers identified in spectra acquired from multiple chromatographic runs were qualitatively compared with offline reference measurements from size-exclusion chromatography. This limitation in real-time applicability was circumvented by chemometric machine learning approaches. The use of convolutional neural networks enabled the selective quantification of the protein monomers and aggregates and delivered superior predictive performance and robustness across cross-validation, independent testing, and synthetic perturbation scenarios compared to traditional chemometric approaches. Collectively, these results demonstrate that the selected Raman spectral markers, combined with advanced chemometric modeling, enable reliable, real-time monitoring of protein size variants in biopharmaceutical downstream processes.

Alkanesulfonates are widely used as anionic surfactants in detergents, requiring accurate quantitative methods for quality control. This study aimed to develop a deuterated internal standard for sodium alkanesulfonates via hydrogen/deuterium (H/D) exchange using a transition metal catalyst. Generally, sulfonate groups are known to strongly adsorb onto metal surfaces and deactivate catalysts due to their catalyst-poisoning effect. However, we found that alkanesulfonates can be deuterated with a ruthenium on carbon (Ru/C) catalyst in D2O under a hydrogen atmosphere. The D contents increased with alkyl chain length, ranging from 20 to 86%. Sodium dodecanesulfonate, which showed the highest D content, was selected as the internal standard. A model detergent sample was prepared to evaluate quantification performance. Quantitative analysis was conducted using liquid chromatography-time-of-flight mass spectrometry (LC-TOFMS) with electrospray ionization (ESI) and field desorption (FD)-TOFMS. ESI provided high sensitivity for trace analysis, while FD offered faster measurements for concentrated samples. Spike-and-recovery experiments across a concentration range (0.50-200 ppm) demonstrated that using an internal standard improved measurement accuracy. This approach offers a practical solution for quantifying sulfonate-based surfactants in complex detergent matrices.

Eco-friendly screen-printed sensor using tapioca-based conductive ink modified with coconut fibers.

Vertex–frequency hypergraph signal processing: Analytic tools and applications

A review on advances in DNA-intercalators for sensor technologies: Mechanisms, applications, and innovations.

Design and application of a high-energy microplasma annular jet for colorimetric detection of Cr(III).

The pathologies pancreatic ductal adenocarcinomas, inflammatory lesions of the pancreas, postpancreatectomy reactive tissue, and recurrent pancreatic ductal adenocarcinomas all express fibroblast activation protein and are hardly distinguishable by static PET using [68Ga]Ga-labeled fibroblast activation protein inhibitors (FAPIs) combined with CT. Dynamic imaging allows full [68Ga]-Ga-FAPI kinetic profile analysis, highlighting differences among these pathologies. Here, we applied a voxel-level digital biopsy approach combined with network analysis and clustering to characterize healthy, nonmalignant pathologic, and malignant pathologic kinetic signatures. Methods: This monocentric, retrospective study included 47 patients (>18 y) with morphologically unclear pancreatic lesions on CT or MRI and supplemental [68Ga]Ga-FAPI-46 PET/CT in a primary (31 patients) or recurrent (16 patients) setting. Lesions were classified according to biopsy results (primary cases) or CT appearance and clinical course (recurrent cases). Digital biopsy samples (300 voxels) of pancreatic lesions and control organs (muscle, fat, kidneys, liver, and blood) were taken and then masked and imported into an open source visual analytics application. Voxel networks were created with multiple digital biopsy samples from a single scan or digital biopsy samples combined from multiple scans, with a minimum Pearson correlation value of 0.7. A k-nearest-neighbor edge reduction was applied before Markov clustering. Datasets were then unmasked for interpretation. Static PET parameters (SUVmax and SUVmean) and time to peak of pancreatic lesions and control tissues were extracted from isotropic volumes and analyzed by a t test (threshold for significance, P = 0.05). Results: This work created 47 individual networks and 2 combined networks. Within individual networks, voxels tended to arrange and cluster within the sampled volume of interest (VOI; left and right kidneys strongly coclustered). Networks typically arranged into healthy controls, elimination organs, and pathologic (malignant and nonmalignant) regions. Pathologies tended to cluster with high purity (>95% from the same VOI), with multiple clusters per VOI, indicating intralesional heterogeneity. Our analysis approach could differentiate between malignant and nonmalignant pathologies in the primary and recurrence settings. This differentiation was driven by slower FAPI clearance within malignant voxels. Conclusion: The kinetics of [68Ga]Ga-FAPI-46 across the different tissues, coupled with this sampling and analysis approach, allowed the separation and identification of healthy, nonmalignant pathologic, and malignant pathologic clusters and kinetic features that may facilitate diagnosis and warrant further investigation.

Continuous and online detection of melamine, 6-thioguanine, and histamine using silver colloid-based SERS.

A 4D-printed, magnetically actuated automated solid-phase extraction device coupled with ICP-MS for multiple trace metal analysis.

ABSTRACT This study investigates whether sub-Saharan Africa’s anomalous financial market structure is due to adverse macroeconomic fundamentals. To this end, it uses comparative statistics to derive a novel analytical framework to first characterize an ideal structure and later explore the features of an aberrant system. In its subsequent empirical analysis that employs panel quantile regression, the paper affirms that the sub-region’s macroeconomic fundamentals mostly explain the mentioned aberration in a nonlinear manner. The paper’s two key contributions are its novel analytical framework and application of a panel quantile methodology to consider the relation between the regressand and regressors.

MXene-based fluorescent aptasensors leverage the synergistic integration of the intrinsic physicochemical properties of MXenes, including tunable surface chemistry, broad-spectrum optical absorption, and superior fluorescence quenching efficiency, with the molecular recognition capabilities and strong binding affinity of aptamers. These two-dimensional transition metal carbides and nitrides efficiently suppress background fluorescence in dye-labeled aptamer systems through electrostatic interactions and π-π stacking. In the absence of the target analyte, the aptamers adsorb onto the MXene surface, facilitating non-radiative energy transfer and thereby suppressing the signal. Upon specific target recognition, a conformational rearrangement of the aptamer reduces its surface affinity, leading to desorption and subsequent fluorescence recovery via a target-induced "signal-on" mechanism. Such platforms demonstrate ultra-low detection limits, excellent selectivity, and modular adaptability for the detection of a broad spectrum of analytes, including clinical biomarkers, pathogenic microorganisms, environmental toxins, and heavy metal ions. This comprehensive review systematically summarises the mechanistic foundations of MXene-aptamer interactions, recent advancements in analytical applications, and emerging directions for translational development in biomedical diagnostics and environmental monitoring.

Current liquid-phase resonant biosensors, such as Quartz Crystal Microbalance, Surface Acoustic Wave, or Surface Plasmon Resonance, typically rely on specialized piezoelectric substrates or complex optical setups. These requirements often necessitate cleanroom fabrication, thereby limiting cost-effective scalability. This study presents a high-integration sensing platform based on standard Printed Circuit Board (PCB) technology, incorporating an embedded inductor within a fluidic system for real-time monitoring. This design leverages industrial manufacturing standards to achieve a compact, low-cost, and scalable architecture. Detection is governed by shifts in the resonance frequency of an LC tank circuit; specifically, increases in bulk ionic strength induce a frequency decrease, whereas biomolecular adsorption at the sensor surface leads to a frequency increase. This phenomenon can be explained by the modulation of the inter-turn capacitance, which is modeled as a combination of capacitive elements accounting for contributions from the bulk electrolyte and the surface-bound dielectric layer. Such divergent responses provide an intrinsic self-discriminating capability, allowing for the analytical differentiation between surface interactions and bulk effects. To the best of our knowledge, this is the first demonstration of an inductor-based resonant sensor fully embedded in a PCB fluidic architecture for continuous liquid-phase analyte monitoring. Validated through a protein-antibody model (Bovine Serum Albumin-anti-Bovine Serum Albumin), the sensor demonstrated a limit of detection of 1.7 ppm (0.026 mM) and a linear dynamic range of 31–211 ppm (0.47–3.2 mM). These performance metrics, combined with a reproducibility of 4 ± 3%, indicate that the platform meets the requirements for robust analytical applications. Its inherent simplicity and potential for miniaturization position this technology as a viable candidate for point-of-care diagnostics in diverse environments.

A concise and efficient second-generation synthesis of 2-(2-(trifluoromethyl)-1H-benzo[d]imidazol-1-yl)benzoic acid (TBBA) has been developed. The synthesis affording enantiomerically pure TBBA atropisomers was significantly streamlined through optical resolution by diastereomeric salt formation, enabling preparation on a 32 mmol scale. The applicability of TBBA as a chiral derivatizing agent in the solid-phase synthesis of amino acid derivatives was demonstrated, allowing determination of the absolute configuration and optical purity by 1H and 19F NMR spectroscopy. Furthermore, 19F NMR analyses were successfully carried out on a low-field benchtop NMR spectrometer, including samples dissolved in nondeuterated solvents.

Precision agriculture technologies (PATs) are reshaping modern crop production by promoting efficient and sustainable farming practices. Core components of precision agriculture include remote sensing, GPS-enabled machinery, variable rate technology (VRT), and Internet of Things (IoT)-based systems. Remote sensing tools and unmanned aerial vehicles provide high-resolution data that support accurate assessment of crop health, soil variability, and pest incidence. GPS-guided equipment improves the precision of field operations such as sowing, fertilizer application, and harvesting, leading to reduced input losses and higher operational efficiency. VRT allows site-specific application of water, fertilizers, and pesticides according to crop demand and real-time field conditions, thereby minimizing excessive input use, nutrient runoff, and greenhouse gas emissions. The adoption of precision agriculture significantly enhances environmental sustainability by conserving water, lowering chemical inputs, and improving soil health. By reducing the ecological footprint of farming while increasing yields and profitability, PATs offer a viable solution for meeting the growing global food demand and ensuring long-term agricultural and environmental sustainability. Precision agriculture integrates advanced technologies to enhance farm productivity, efficiency, and sustainability. Precision farming, defined by the strategic application of data analytics and advanced technologies, has emerged as a transformative approach for achieving sustainable agriculture. precision agriculture provides an effective pathway for building sustainable and resilient agricultural systems by improving resource-use efficiency, while simultaneously supporting food security and environmental sustainability.

ABSTRACT: Traditional food retail outlets in India(Amravati) function largely without digital systems and structured data, relying mainly on experience and intuition for decision-making. This study examines the application of basic data analytics in a fully offline traditional food outlet, Nanakramji Refreshment Centre, located in Amravati, Maharashtra. The objective of the research is to understand consumer preferences, satisfaction drivers, and purchase behavior in a heritage-based food retail context. An exploratory–descriptive research design was adopted. Primary data were collected from 50 respondents using a structured questionnaire, supported by relevant secondary sources. Descriptive statistics along with inferential techniques such as Multivariate Analysis of Variance (MANOVA) and the Chi-square test were used for data analysis. The findings reveal that product attributes such as taste consistency, freshness, and hygiene significantly influence consumer purchase decisions and revisit intention (p < 0.05). Price was found to be a secondary consideration. The study demonstrates that simple data analytics can generate meaningful insights even in non-digital traditional businesses. Keywords: Data analytics, Traditional food retail, Consumer behavior, Street food, Customer satisfaction

Relevance. The improvement of forecasting and planning technologies, the development of proactive human resource management are a strategic necessity for competitive developing organizations. The shortage of human resources, high competition in attracting talent, and the growing expectations of employees for working conditions and career growth require successful employers to have an objective scenario vision of the future development of the organization and staff. The transformation of the HR function from an operational role to a strategic business partnership defines the transition from reactive problem solving to forecasting future skill needs, predicting and preventing turnover, optimizing headcount planning, increasing engagement, and other promising staff characteristics. The purpose of the study is to substantiate the directions of improving forecasting and planning technologies in proactive human resource management in the modern labor market. Objectives: to study the development trends of the modern national and regional labor market; to analyze reactive and proactive approaches to personnel management in a labor-deficient market environment; to reveal the differences between forecasting and planning tasks in the management process; to determine the directions of application and development of predictive HR analytics in proactive human resource management of the organization. Methodology. The research was conducted on the basis of general scientific methods of analysis and synthesis, a systematic approach and comparative analysis, methods of statistical analysis, forecasting and planning, etc. Results. The problematic situations of the formation of the labor shortage conjuncture of the modern national and regional labor market are diagnosed, in accordance with the diagnostic results, the advantages of proactive personnel management are revealed. The directions of development of predictive and prescriptive HR analytics as a basis for improving forecasting and planning technologies are considered. Conclusions . In the context of the labor-deficient conditions of the modern labor market, the main focus of improving forecasting and planning technologies in proactive management is the priority development of predictive and prescriptive HR analytics for systematic and comprehensive forecasting of the prospects for the development of an organization's human resources.

Cyclone flow is a technique to evaporate the liquid at room temperature. Efficient milliliter-to-microliter sample concentration at room temperature is expected for high-sensitivity analysis in biology and chemistry; however, it is hindered by the difficulty of quantifying the volume in real-time under the cyclone flow. This research targets a non-contact, vision-based system using a physics-guided machine learning (PGML) framework to precisely monitor and control this process under intense cyclone flow. The key is a physics-informed loss function that embeds the container's geometric constraints into the neural network's training, substantially enhancing model robustness and accuracy. Experimental results demonstrate the PGML model's superiority, achieving a nearly 70% reduction in error compared to purely data-driven methods. The system shows a measurement error of just 1.2% and a coefficient of variation of 1.5% at a 20 µL target, meeting stringent bioassay requirements. This work establishes a powerful solution for automated and precisely quantitative sample concentration, promising to advance a wide range of analytical applications.