Transition-metal (TM)-doped MXenes stand out as highly promising earth-abundant electrocatalysts for the hydrogen evolution reaction (HER), paving the way for cost-effective and sustainable green hydrogen generation. This review surveys the latest developments in TM-doped MXene systems for HER, emphasizing how diverse fabrication routes, including in situ incorporation during MAX-phase synthesis and post-etching modifications via atomic layer deposition (ALD), plasma-enhanced chemical vapor deposition (PECVD), and electrochemical deposition, shape their electronic properties and catalytic efficiency. Insights from d-band center theory, operando spectroscopic techniques, and deliberate active-site tuning are integrated to illustrate pathways toward optimal hydrogen binding energetics (ΔGH* ≈ 0 eV). Benchmark comparisons reveal that carefully engineered TM-doped MXenes frequently deliver overpotentials, Tafel slopes, mass activities, and other metrics that approach or outperform conventional noble-metal catalysts. The review also critically evaluates ongoing limitations, such as susceptibility to oxidation, dopant instability, and nanosheet restacking, while exploring architectural innovations and protective modifications as effective countermeasures. Looking ahead, the discussion highlights transformative opportunities in machine-learning-guided discovery, exploitation of quantum phenomena, and nature-inspired fabrication routes to bridge the gap toward practical, large-scale implementation.
One of the main challenges in welding dissimilar joints between steels and nickel-based alloys for power engineering applications is carbon diffusion and the formation of brittle interfacial phases. The aim of this work was to evaluate the effect of the buttering layer composition on the microstructure and mechanical behavior of welded joints between AISI 304 H steel and Inconel 617 alloy. Multi-pass Gas Tungsten Arc Welding (GTAW) using Inconel 617 filler, with prior application of Inconel 82 and Inconel 617 buttering layers on the AISI 304 H steel side, was performed and compared with the structural integrity of joints produced without buttering using conventional and pulsed GTAW processes. The welded joints were characterized by optical microscopy (OM), scanning electron microscopy (SEM) coupled with energy-dispersive X-ray spectroscopy (EDS), tensile testing at room and elevated temperatures, Vickers hardness measurements, and Charpy impact testing. Significant metallurgical interactions and local element diffusion during solidification and cooling are reflected in the presence of Type I and Type II grain boundaries as well as intricate morphological characteristics including peninsula structures and isolated austenitic islands near the interfaces. Considerable diffusion of Ni, Cr, and Fe, as well as localized segregation near the AISI 304 H/buttering interface, was observed. Additionally, TiC and NbC precipitates were detected in the Inconel 82 buttering layer, and the Inconel 617 buttering and weld metal were enriched with Cr and Mo-based carbides (M₂₃C₆, Mo₆C). The application of a buttering layer significantly improved the mechanical performance of AISI 304 H-Inconel 617 welded joints. The buttering process resulted in increased and more uniform hardness across the weld metal ( 221 HV0.5 for IN82 and 239 HV0.5 for IN617 in non-buttered welds, with higher values observed in buttered welds). At room temperature, the IN617 buttered joint exhibited the highest ultimate tensile strength (UTS) of approximately 682 MPa, compared with 581 MPa for the IN82 buttered joint. The IN617 buttered joint also exhibited significantly higher ductility ( 50
In tribal and climate-vulnerable regions, limited access to agricultural technologies and resource constraints often hinder crop diversification, despite its recognised role in enhancing sustainability, resilience, and food security. While technology adoption is known to influence farm diversification, evidence specific to millet-based systems in India’s tribal areas remains scarce. This study examines the determinants of crop diversification and the role of agricultural technology adoption among millet farmers in Koraput district, Odisha, a climate-sensitive and predominantly rainfed tribal region. Using primary data from 500 farm households, two composite indices were constructed: the crop diversification index (CDI), derived from Simpson’s index, and the agricultural technology intensity index (ATII), capturing physical, chemical, and strategic mechanisation dimensions. A fractional heteroscedastic probit model was applied, controlling for socio-economic and institutional factors. Results indicate that ATII has a statistically significant and positive association with CDI. Operational landholding size and age are also positively related to diversification, although their effects are comparatively modest. The heteroscedastic specification further shows that technology adoption, family size, and institutional barriers influence the variability of diversification outcomes, highlighting the role of unobserved heterogeneity. Moreover, the instrumental variable diagnostics demonstrate that correcting for endogeneity strengthens the estimated causal impact of ATII on CDI. Findings emphasise the need for promoting location-specific technologies, strengthening extension services, and enhancing targeted institutional support to scale up diversification and enhance climate resilience in millet-based farming systems.
The development of efficient and robust catalysts for overall water splitting is vital for the advancement of hydrogen-based renewable energy systems. This work reports the synthesis of beta-NaGdF4:Yb3+/Er3+ upconversion nanoparticles (UCNPs) via a solvothermal method and demonstrates their bifunctional catalytic activities toward both the hydrogen evolution reaction (HER) and the oxygen evolution reaction (OER), as well as photoelectrochemical (PEC) water splitting. Electrochemical studies reveal that the UCNPs exhibit low overpotentials and high exchange current density with favorable Tafel slopes for both HER and OER, combined with excellent charge-transfer properties and abundant active sites. Especially, under irradiation, the UCNPs exhibit enhanced PEC activities, benefiting from their strong light-matter interactions and photon upconversion capability, which facilitates the absorption of near-infrared photons and converts them into visible emission, thereby enhancing charge carrier dynamics. The bifunctional NaGdF4:Yb3+/Er3+ electrodes also attain efficient overall water splitting, requiring similar to 1.52 V to reach 10 mA cm-2 under simulated solar irradiation with remarkable stability over 24 h. To the best of our knowledge, this is the first demonstration of NaGdF4:Yb3+/Er3+ UCNPs acting as a bifunctional catalyst for electrochemical and photoelectrochemical overall water splitting, offering new avenues toward full-spectrum solar energy harvesting and sustainable hydrogen production.
Brain tumors are fatal and severely disrupt brain function as they advance. Timely detection and precise monitoring are crucial for improving patient outcomes and survival. A smart healthcare system leveraging the Internet of Medical Things (IoMT) revolutionizes patient care by offering streamlined remote healthcare, especially for individuals with acute medical conditions like brain tumors. However, such systems face significant challenges, such as (1) the increasing prevalence of cyber attacks in the expanding digital healthcare landscape, and (2) the lack of reliability and accuracy in existing tumor detection methods. To address these issues, we propose Secured Brain Tumor Detection (SBTD), the first unified system integrating IoMT with secure tumor detection. SBTD features: (1) a robust security framework, grounded in chaos theory, to safeguard medical data; and (2) a reliable machine learning-based tumor detection framework that accurately localizes tumors using their anatomy. Comprehensive experimental evaluations on different multimodal MRI datasets demonstrate the system's suitability, clinical applicability and superior performance over state-of-the-art algorithms.
