Heterogeneous catalytic pathways for clean energy conversion involve thousands of elementary steps, but most quantum-mechanical models involve only a few dozen reactions. We combine extensive density functional theory (DFT) calculations, machine learning (ML) for activation barrier prediction, and human intelligence-inspired reaction enumeration and elementary reaction identification. This enables automated kinetic modeling of CO2 hydrogenation on copper, a key process to produce fuels and chemicals. We construct the largest dataset of 152 elementary CO2 reduction reactions and experimentally determine CO2 conversion, finding that even large networks with 100+ reactions are insufficient. In contrast, our approach reveals 9389 elementary reactions, reducing human bias in the reaction pathway. We unravel 40-fold higher CO2 conversion rates, following experimental trends of methanol and CO production. We establish the crucial role of intermolecular hydrogen transfer and hydrogenation by molecular hydrogen, a surprising ML-enabled discovery validated post-facto. The proposed strategy to comprehensively model complex catalytic mechanisms will significantly advance catalysis research and carbon conversion processes.
Understanding the thermal behavior and reaction kinetics of petroleum pitch is crucial for the design and optimization of pyrolysis, gasification, and combustion reactors that utilize it as a feedstock. The thermogravimetry analysis (TGA) was conducted to investigate the combustion and pyrolysis of petroleum pitch. A single-step and two-step mass loss were observed in the pyrolysis and combustion, respectively. The distributed activation energy model (DAEM) was applied to study the pyrolysis and combustion kinetics. The average activation energy (E) values were 188.029 and 139.280 kJ mol−1 at pyrolysis and combustion, respectively. The variation of activation energy and pre-exponential factor confirmed the complicated nature of the pyrolysis and combustion. The positive values of Gibbs free energy and enthalpy indicated the endothermic nature of transient state formation reactions in the pyrolysis and combustion processes. A negative value of entropy was obtained in both the cases of pyrolysis and combustion. The comprehensive combustion indexes were evaluated, and the combustion of petroleum pitch was favored. The kinetic compensation effect (KCE) relationship was followed at pyrolysis and combustion.
Efficient and stable enzyme systems are crucial for lignocellulosic biomass conversion, yet they remain a significant limitation in second-generation biofuel production. Here, we applied a weighted sum matrix (WSM)-based multi-criteria screening strategy to identify superior cellulolytic fungus from a diverse collection of natural isolates. This approach revealed Talaromyces marneffei as a previously unreported hyper-cellulolytic organism. The T. marneffei secretome exhibited nearly two-fold higher biomass saccharification efficiency than a commercial enzyme preparation under industrially relevant conditions. Label-free quantitative nano-LC-MS/MS analysis identified 549 secreted proteins, including 201 carbohydrate-active enzymes (CAZymes) spanning multiple glycoside hydrolase and auxiliary activity families. Quantitative profiling revealed a high abundance of key cellulolytic enzymes, particularly GH7 cellobiohydrolase I (CBH1), along with diverse accessory enzymes enabling synergistic biomass deconstruction. The enzyme concoction displayed broad operational robustness, with optimal activity at acidic pH (4-4.5), elevated temperature optima (60-70 °C), and exceptional storage stability, retaining over 96% activity after 60 days at ambient temperature. Molecular modeling and dynamics simulations of CBH1 revealed an open, flexible active-site architecture with enhanced ligand interactions, providing mechanistic insight into its thermostability. Collectively, this study highlights T. marneffei as a promising source of industrially relevant lignocellulolytic enzymes and demonstrates the value of proteomics-driven secretome analysis for enzyme discovery.
Artificial intelligence has transformed social media sites like Twitter, making them useful tools for smart personality prediction. While transformer models like BERT and RoBERTa provide solid results, they tend to be computationally expensive, slower to train and suffer from imbalanced data. To mitigate these limitations, this paper proposes a hybrid ensemble strategy that leverages powerful contextual representations from transformers alongside the efficiency and speed of machine learning methods. Not only does this approach reduce computation time and memory complexity, but it also enhances robustness and scalability. Experimental results on Twitter data prove that the suggested model performs better than previous methods, with 90% accuracy and 91% precision, illustrating its efficiency in predicting personality characteristics based on social media content.
Knowledge of the kinetics of petroleum pitch pyrolysis is crucial to improve and optimize the design of pyrolysis reactors, gasification reactors, and circulating fluidized bed combustors (CFBC) to use as feedstock. Thermogravimetric analysis (TGA) was performed to understand the kinetic aspects of the pitch pyrolysis. The isoconversional Friedman, Kissinger–Akahira–Sunose (KAS), Flynn–Wall–Ozawa (FWO), and Starink methods were used to calculate the divergence of activation energy and mechanistic understanding of the complex nature of pitch pyrolysis. The mean activation energies were 176.46, 178.79, 176.79, and 189.90 kJ mol−1 for the KAS, FWO, Starink, and Friedman methods. The three-dimensional diffusion model, followed by the pyrolysis process, was determined using model-based Coats–Redfern and Criado methods. The linear relationship of the pre-exponential factor and the activation energy was obtained through the kinetic compensation effect (KCE) relationship. The positive values of enthalpy and Gibbs Free energy confirmed the endothermic nature of the pitch pyrolysis. The positive to negative variation of entropy was observed because of the complicated nature of the pitch pyrolysis process. During pyrolysis, the residue's surface morphology detected pores, cracks, and mesophase formation in scanning electron microscope (SEM) analysis, and sulfur, vanadium, and nickel were present in energy-dispersive spectrometer (EDS) analysis of the residue. Fourier transform infrared spectroscopy (FTIR) analysis of the sample and residue showed an intermediate cross-link reaction, which led to the formation of a mesophase coke structure in the pyrolysis process.
