Xavier Institute of Engineering

Conventional MoS2 catalysts face challenges, including weak piezoelectric response, reliance on energy-intensive ultrasound, and limited active sites, reducing their efficiency in degrading organic pollutants. In this work, 1T/ 2H heterostructured MoS2 nanoflowers were synthesized via an ionic liquid-assisted hydrothermal method, enabling efficient piezocatalytic activation of peroxymonosulfate (PMS) under mild mechanical stirring. The 1T/ 2H MoS2/PMS system rapidly degraded 95.9 % of Rhodamine B in 15 min, with a kinetic rate 4.43 times higher than conventional MoS2, and showed excellent cycling stability. This superior performance was due to enhanced piezoelectric polarization at heterointerfaces, which improved charge carrier separation, and the use of low-energy mechanical stirring instead of ultrasound for activation. Optimized interfacial charge transfer pathways also boosted the generation of superoxide anions (center dot O2-) and singlet oxygen (1O2). Moreover, immobilizing 1T/2H MoS2 on conductive carbon cloth enhanced recyclability and operational feasibility, offering a sustainable solution for water treatment technologies.

The efficient handling of large volumes of low-concentration coal mine methane is crucial for safety and the environment protection. Currently, the crushing and forming processes of traditional bulk supports would reduce the distribution and exposure degree of active components, resulting in lower catalytic activity. Looking for materials with a self-supporting function and hierarchical pore structure as a methane catalyst support is an effective strategy. In this study, a series of PdO-PtO/Al2O3 monolithic catalysts with hierarchical pore structures were obtained by sol-gel method combined with post-impregnation method. Pd3PtOx/Al2O3 monolith with both economy and high activity was optimized. At a space velocity of 12000 mL center dot g-( 1)center dot h-( 1), this catalyst exhibited the highest performance, with methane conversion temperatures of 298 degrees C (T-50) and 347 degrees C (T-90). This high catalytic activity is attributed to the catalyst's self-supporting structure with hierarchical pores. The macropores enhance the contact opportunity between methane and the active sites of the catalyst; while the mesopores provide more active sites, thereby improving the catalytic activity. The self-supporting structure of the support itself improves the stability of the catalyst. This study provides a scientific basis for catalytic oxidation to reduce coal mine methane emissions.

Electrochemical water splitting is regarded as a highly promising technology in hydrogen production methods. Its practical application mainly depends on the development of efficient and durable electrocatalysts to achieve overall water splitting (OWS). However, a pivotal and urgent issue of the current electrocatalysts is the inefficient electron transport pathways and the consequent transmission barriers, which notably restrict the efficiency of catalytic reactions. In this work, a series of NF@Ni9S8/CoMo2S4 honeycomb porous nanosheets grown on nickel foam (NF) carriers was successfully synthesized by a simple two-step hydrothermal method. It is worth noting that the synthesized NF@Ni9S8/CoMo2S4-5 electrode exhibits an oxygen evolution reaction (OER) overpotential of only 308 mV at a current density of 50 mA cm(-2) and a hydrogen evolution reaction (HER) overpotential of only 168 mV at a current density of 10 mA cm(-2) under 1 M KOH. In addition, as a bifunctional catalyst, it requires only 1.72 V to drive 50 mA cm(-2) of OWS and exhibits remarkable long-term stability. The achievement of such excellent performance stems from the following two reasons. From a microstructural perspective, the honeycomb porous nanosheet structure enables the provision of multi-channel transmission paths, offering the best channels for electron transmission. The high specific surface area of this structure provides more active sites for the reaction and also promotes better contact between the catalyst and the electrolyte. From an electronic structure standpoint, sulfidation modulates the electronic interaction between Co and Mo, lowering the energy barrier of the reaction and thereby enhancing catalytic performance. Furthermore, DFT calculations further demonstrated the synergistic effect of Ni9S8 and CoMo2S4 in the catalytic electrolysis of water. This research not only introduces a new strategy for synthesizing highly efficient bifunctional electrocatalysts but also advances the development of electrochemical water splitting applications.

Reactive dividing wall column (RDWC) integrates the advantages of high conversion efficiency, low energy consumption, and reduced investment. However, this further intensification of reaction and separation increases system coupling and significantly complicates process optimization. To address this challenge, this work proposed a machine learning-aided multi-objective optimization (ML-MOO) framework for determining the optimal RDWC design. Taking the dichlorosilane anti-disproportionation RDWC process as a case study, random forest (RF), backpropagation neural network (BPNN), and support vector machine (SVM) were integrated with multi-objective particle swarm optimization (MOPSO) algorithm to optimize the steady-state operating parameters of RDWC. The hyperparameters of three ML models were tuned using Bayesian optimization algorithm (BOA) with 5-fold cross-validation. The results showed that, compared with the rigorous Aspen simulation-based optimization, the SVM surrogate model reduces total annual cost, flow rate of silicon tetrachloride, and environmental impact potential of energy by 5.3 %, 23.5 %, and 7.6 %, respectively, while reducing computation time by 19.3 %. Additionally, due to the existence of internal reactions, the dynamic behavior of RDWC is constrained by both product quality and safety redundancy. To address this, a distributed MPC (DMPC) strategy was proposed, using two sub-MPC controllers to separately control inventory and quality loops, thereby enhancing system fault tolerance. Dynamic response results indicated that benefiting from communication between sub-controllers, the integral absolute error (IAE) value of linear DMPC structure based on linear time-invariant state space (LTI-SS) model differs from that of centralized MPC (CMPC) structure by only 3 % to 10 %, demonstrating similar dynamic response performance while achieving enhanced safety.

Abstract Existing attendance and payroll management systems based on Radio Frequency Identification (RFID), Biometrics or cloud infrastructure possess critical vulnerabilities such as proxy marking, card cloning, spoofing and uninterrupted internet dependency. This exposes organizations to fraudulent access as well as data integrity risks. This paper proposes ECHO, a hybrid edge-cloud attendance and access validation system. This system is designed to enforce a secure and multi-layered authentication for payroll integration. It uses a triple-layer verification pipeline consisting of RFID card validation with secrets, a dual biometric face recognition via InsightFace on both wired camera as well as CCTV stream and a physical line cross detection using YOLOv8 and ByteTrack. The data is processed on Raspberry Pi Edge Node with a midnight batch synchronization to cloud storage. Experimental evaluations across multiple trials yielded a False Acceptance Rate of 0%, True Acceptance Rate of 86.67% and overall face recognition accuracy of 93.33% when operating without internet connectivity. The average latency of face recognition is approximately 918 ms. This demonstrates a reliable, cost effective as well as internet resilient solution for attendance and access tracking which can be eventually integrated with a payroll management system.