Institute of Engineering and Technology, DAVV

Photothermal catalysis has gained significant attention as a research hotspot area in recent years. Cu/ZnO/Al2O3, an important industrial catalyst for the water-gas shift reaction and methanol synthesis, has been extensively studied for decades. Considering the localized surface plasmon resonance characteristic inherent to Cu species within this catalyst, it is anticipated to display favorable photothermal catalytic performance. However, no relevant experimental reports have been published to date. Herein, we investigated a commercial Cu/ZnO/Al2O3 catalyst for the photothermal CO2 hydrogenation reaction. Unexpectedly, and yet reasonably, the catalyst achieved 100% CO selectivity with a CO production rate of 471.4 mmol g(cat)(-1) h(-1) under 1.5 W cm(-2) light irradiation, which is 3.2 times higher than that obtained in the dark at the same catalyst surface temperature of 342.9 degrees C. The enhanced performance can be attributed to the synergistic effect of Cu and Zn that promotes photothermal conversion, facilitates the separation of photogenerated carriers, accelerates reactant activation, and thus drives the reaction forward. In situ/operando spectroscopies confirm that the RWGS reaction over Cu/ZnO/Al2O3 follows a redox mechanism, in which Cu species act as the active sites for CO2 conversion, with the redox transformation being Cu2+ <-> Cu-0/Cu+. This study provides a comprehensive evaluation of the photothermal CO2 hydrogenation performance of a commercial Cu/ZnO/Al2O3 catalyst, offering valuable mechanistic insights into the photothermal CO2 hydrogenation process within the Cu/Zn/Al systems.

Increasing global demand for energy, together with the gradual depletion of fossil fuel resources, highlights the need for sustainable hydrogen production technologies such as electrocatalytic water splitting. In this study, a MnCo2O4@MoS2 composite nanohybrid was synthesized via hydrothermal method followed by calcination approach under an argon atmosphere to enhance its oxygen and hydrogen evolution reaction (OER& HER) performance. Detailed analysis showed that combining MoS2 with the spinel MnCo2O4 substantially enhanced the composite's electrocatalytic properties. OER composite exhibits low Tafel slope of 53 mV/dec and a low overpotential of 307 mV to achieve 30 mA/cm2 current density in 1 M KOH alkaline medium, whereas, for HER the composite electrocatalyst deliver -392 mV overpotential and low overpotential of 89 mV/dec and these values are much lower than that of compared pristine materials making the composite more efficient. Additionally, the MnCo2O4@MoS2 catalyst demonstrated outstanding long-term durability for OER and HER respectively of continuous operation, underscoring its resilience in alkaline and acidic conditions. The study offers an effective and stable catalyst for renewable energy. These results highlight the promise of such composites in helping to solve the energy challenge and progress clean electrolysis technologies.

Rapid urban expansion significantly alters local environmental conditions, notably elevating Land Surface Temperature (LST), which poses challenges for sustainable urban development. Accurate monitoring and prediction of LST dynamics are therefore essential for effective urban planning and environmental management. While numerous studies have analyzed LST dynamics using remote sensing and machine learning, most rely on complex workflows involving data export and offline modeling. This research introduces a fully cloud-native framework that leverages the Google Earth Engine (GEE) platform and its built-in Random Forest (RF) algorithm to analyze and predict annual mean LST in Lucknow, India, from 2014 to 2024. The entire workflow-including data acquisition, preprocessing, predictor generation, model training, validation, and spatial prediction-was conducted directly within GEE, eliminating the need for external computation. This makes the approach highly reproducible and transferable, enabling researchers to predict LST for any region of interest simply by modifying the ROI and date range in the shared GEE code. Using Landsat 8 Collection 2 Level 2 products, we trained an RF regression model (150 trees, N = 32,054 samples) with spectral indices (NDVI, NDMI, NDWI, UI, TCB, Albedo) and topographic variables (elevation, slope). Results revealed distinct spatial heterogeneity in LST, with higher temperatures in built-up areas and cooler conditions in vegetated zones. The model achieved strong predictive performance (Testing R2 = 0.826, RMSE = 0.896 degrees C), with even higher accuracy in 2014 and 2024 (R2 approximate to 0.91, RMSE approximate to 0.6 degrees C). This study not only validates RF as a reliable predictor of LST but also delivers a scalable, open-access, GEE-based framework for rapid, location-independent LST prediction, offering valuable insights for urban heat mitigation and sustainable city planning. (c) 2026 COSPAR. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

In this work, wavelet-based filtering operators are constructed by introducing a basic function D(t_1, t_2, t_3) using a general wavelet transform. The cardinal orthogonal scaling functions (COSF) provide an idea to derive the standard sampling theorem in multiresolution spaces which motivates us to study wavelet approximation analysis. With the help of modulus of continuity, we establish a fundamental theorem of approximation. Moreover, we unfold some other aspects in the form of an upper bound of the estimation taken between the operators and functions with various conditions. In that order, a rate of convergence corresponding to the wavelet-based filtering operators is derived, by which we are able to draw some important inferences regarding the error near the sharp edges and smooth areas of the function. Eventually, some examples are demonstrated and empirically proven to justify the fact about the rate of convergence. Besides that, some derivation of inequalities with justifications through examples and important remarks emphasizes the depth and significance of our work.

Recently, the global oil and gas industry has experienced a sharp rise in cybersecurity incidents targeting critical operational technology (OT) infrastructure. Attacks over the past five years have grown increasingly sophisticated, resulting in production losses, financial damage, equipment destruction, and in severe cases, fatalities. Traditional information technology security solutions remain inadequate for OT environments and often worsen vulnerabilities by expanding the attack surface. This study presents a hybrid blockchain framework that combines public and private blockchain characteristics and integrates with existing OT cybersecurity infrastructure. The framework is intended to help prevent incidents that lead to plant upsets, costly shutdowns, explosions, and loss of life. Through experimental validation across four operational scenarios, the prototype built using the Quorum Byzantine Fault Tolerance (QBFT) consensus mechanism achieved 92% precision and 86% recall for malicious event classification. Precision and recall quantify classification accuracy, while the detection rate reported in the scenario analysis represents detection coverage relative to total write attempts. Under sustained adversarial load in Scenario 4, the framework increased detection coverage from the 27.35% baseline observed in Scenario 1 to 57.33%. A chi square test ( chi 2=4709.69 , df=3 , N=38,231 , p<0.001 ) confirmed significant differences in detection outcomes across scenarios, indicating that performance depended on the scenario configurations defined in the framework. The system maintained process control compatible latencies between 24 and 368 ms and required only 8.15 KB per second of network bandwidth. These results demonstrate the framework's suitability for real time industrial use and suggest potential annual financial savings of 973 million to 1.99 billion dollars based on recent ransomware incident analyses.