Sharda University

MXene quantum dots (MQDs) provide a chemically versatile platform for metal-ion sensing because their surface coordination environments, defect-associated states, and size-dependent electronic structure can couple molecular recognition to measurable optical responses. This review develops a mechanism-centered framework for understanding that coupling rather than treating sensing performance as a collection of isolated analytical metrics. It first examines how surface terminations, coordination-site accessibility, electronic-state organization, solvation, and interfacial binding thermodynamics define the recognition landscape of MQDs. It then analyzes how recognition events are translated into fluorescence or electrochemiluminescence responses through charge transfer, energy transfer, static or dynamic quenching, inner-filter effects, and related excited-state perturbations. Particular emphasis is placed on structure–property–signal relationships, mechanistic distinguishability, and trade-offs among sensitivity, selectivity, reversibility, and robustness. Evidence from reported metal-ion sensing systems is comparatively assessed to identify recurring design trends and limitations in mechanistic validation, cross-platform benchmarking, and real-matrix translation. The analysis indicates that future progress depends less on maximizing signal magnitude than on establishing reproducible coordination environments, experimentally resolved transduction pathways, and standardized performance criteria. This perspective positions MQD sensing as a recognition-to-signal engineering problem governed by coupled surface chemistry and excited-state dynamics.

Accurate prediction of the Klinkenberg slippage factor (KSF) is crucial for characterizing low permeability gas reservoirs, but conventional laboratory determination methods are prohibitively costly and time-consuming, constituting a major research gap. This study addresses this limitation by presenting a novel, experimentally validated workflow that seamlessly integrates comprehensive laboratory measurements with advanced machine learning (ML) models to achieve rapid and accurate KSF prediction. A robust dataset of 253 limestone core samples was utilized, employing air permeability, cation exchange capacity (Qv), porosity, tortuosity and grain density as input features. After a systematic comparative evaluation of ten algorithms on the capacity of ML to model the complex, non-linear relationships in KSF estimation are demonstrated. The key distinction and novelty reside in the superior performance of the AdaBoost algorithm, which achieved the highest predictive accuracy (R2=0.998, AARE%=1.276). This research offers a robust, efficient, and cost-effective alternative to traditional permeability correction techniques, establishing an innovative, data-driven framework that significantly enhances predictive modeling for reservoir evaluation in tight gas formations.

Combining AI with forensic accounting and ESG reporting has been one of the biggest developments in modern financial regulation. This research explores how AI can increase the accuracy of fraud detection, and improve the reliability and completeness of ESG disclosures, in light of the current limitations associated with traditional audit techniques and the piecemeal nature of sustainability frameworks. Based upon Institutional Theory, Dual-Process Theory, and Explainability and Accountability Theory, a mixed-methods approach using survey responses from 342 respondents, structural equation modelling, and interview data was used to analyze the effects of AI on organizational performance. AI governance adequacy was found to be the strongest predictor of performance; and the best way to deploy AI was determined to be a model of human-AI complementarity, rather than complete automation.

The two pyrazole derivatives have been regioselectively synthesized and characterized by single crystal X-ray diffraction study. The study revealed that the compounds crystallized in the monoclinic crystal system with the space group P21/n for A1 and P21/c for A2. The electronic structure of the two regioisomeric forms of pyrazole compounds A1 and A2 was predicted by the DFT calculations utilizing a M06-2X/def2-TZVP basis set. The molecular stability and reactivity have been evaluated using frontier molecular orbital analysis. Molecular electrostatic potential (MEP) analysis shows the sites of nucleophilic and electrophilic reactivity. In addition, Hirshfeld surface analysis was performed and two-dimensional fingerprint plots were calculated to visualise intermolecular interactions in the crystal structure. Hirshfeld analysis and fingerprint plots enable the identification of H & sdot;& sdot;& sdot;H and C & sdot;& sdot;& sdot;H contacts as major interactions with significant difference in percent contribution in the two molecules. Docking studies predicted that A1 and A2 have suitable structures to target SARS-CoV-2 protein (PDB ID: 2D2R), which was further validated using molecular dynamics studies.

The dynamic interplay between tumor cells and immune components within the tumor microenvironment (TME) is a critical determinant of cancer progression. At the tissue and cellular levels, immune surveillance mechanisms mediated by cytotoxic T lymphocytes (CTLs) and natural killer (NK) cells are responsible for recognizing and eliminating transformed cells. However, tumor cells remodel their microenvironment to establish an immunosuppressive niche that supports immune evasion. Increasing evidence identifies long non-coding RNAs (lncRNAs), particularly members of the small nucleolar RNA host gene (SNHG) family, as key regulators of tumor-immune cell interactions. This review focuses on the cellular and tissue-specific mechanisms by which SNHG lncRNAs (including SNHG1, SNHG3, SNHG4, SNHG9, SNHG10, SNHG12, SNHG14, SNHG15, SNHG17 and SNHG20) modulate the immune landscape across diverse malignancies. Mechanistically, SNHGs function through competitive endogenous RNA (ceRNA) networks, signaling pathway modulation, and epigenetic regulation to influence immune cell differentiation and activity within the TME. They promote regulatory T cell (Treg) expansion, drive macrophage polarization toward the M2 phenotype, suppress NK cell cytotoxicity, impair CTL responses, and enhance expression of immunosuppressive mediators such as PD-L1 and IL-6R. By reshaping immune cell composition and function at the tissue level, SNHG lncRNAs contribute to the establishment of an immunologically permissive tumor niche. Understanding these cellular regulatory circuits highlights SNHG family members as potential modulators of tumor-immune crosstalk and as promising targets for microenvironment-directed therapeutic strategies.