Ni single-atom catalysts have been widely explored for CO₂ reduction, however, their practical application is often hampered by complex synthesis and instability at high current densities. In this context, well-dispersed nickel nanoparticles present a compelling alternative, offering both facile fabrication and robust performance. Herein, a hierarchical catalyst comprising nickel nanoparticles encapsulated within a nitrogen-doped carbon shell on a hollow-rod carbon substrate (denoted as Ni_NP-BCN@C) was designed. The hollow-rod architecture maximizes the exposure of nickel nanoparticles as active sites, while the nitrogen-doped carbon shell effectively modulates the electronic environment of the metallic Ni, suppressing the competing hydrogen evolution reaction and promoting CO₂ activation. The catalyst exhibits exceptional CO₂-to-CO conversion, with a Faradaic efficiency exceeding 90% at −0.83 V vs. RHE in an H-cell and remarkable stability over 32 h. When evaluated in a flow-cell configuration, it achieves a CO Faradaic efficiency > 98% at a current density of 300 mA∙cm⁻², corresponding to a high turnover frequency of ~93,579 h⁻¹. In situ Fourier transform infrared spectroscopy revealed intensified bands for key intermediates (*COOH and COO⁻), confirming enhanced CO₂ adsorption and activation. This work showcases a scalable and efficient catalyst design, highlighting the synergy between structural engineering and electronic modulation for advanced CO₂ electroreduction.

Polyethylene (PE) plastics pose a severe environmental challenge due to their recalcitrance, which also creates an extremely nutrient-limited environment for microbial colonizers. Understanding the initial adaptive mechanisms is crucial for developing effective bioremediation strategies. In this study, comparative transcriptomics was employed to analyze the specific adaptive response of Bacillus velezensis C5 to PE microplastics by comparing it with responses to a non-polymeric hydrocarbon, n-docosane (C22), and a nutrient-free medium. The results revealed a robust, PE-specific transcriptional program distinct from general starvation or surface-contact responses. Exposure to PE, but not C22, triggered massive upregulation of genes involved in biofilm formation (e.g., the epsA-O operon, including a 185-fold increase in epsL) and sporulation (e.g., a 22-fold increase in yicZ), indicating a polymer-induced strategy for surface colonization and long-term persistence. Although genes associated with canonical PE degradation were not induced, several LLM-class monooxygenases were upregulated, suggesting an auxiliary role in stress mitigation. Phenotypic analyses confirmed that strain C5 remained viable without growth on PE, formed a dense biofilm that increased surface hydrophilicity, and induced only subtle physicochemical changes to the plastic. This study demonstrates that the initial interaction is dominated by a sophisticated, polymer-specific survival program rather than direct metabolic degradation. This insight is fundamental for designing future strategies capable of overcoming this adaptive phase to promote efficient plastic biodegradation.

Complex distillation processes can often be effectively optimized using meta-heuristic algorithms. However, during optimization procedure, a large number of infeasible solutions are generated, hindering efficient exploration of feasible, high-performance regions of the search space. In this study, we propose a data-driven identification and adaptive directed correction strategy for handling infeasible solutions, and on this basis, develop an efficient multi-objective optimization framework (MO-DIDC) for complex distillation processes. By identifying infeasible solutions that closely resemble high-performance ones, the framework leverages them to accelerate convergence to optimal designs. A surrogate model is trained to distinguish high- and low-performance solutions and is then used to identify potentially high-performance candidates within the infeasible set. Through similarity analysis, the most influential variable is selected for correction to generate new promising solutions. This strategy reduces unnecessary exploration of infeasible regions and concentrates computational effort on feasible, high-quality solutions. Demonstrated on a side-stream double-column extractive distillation system and a four-column extractive distillation system, the proposed optimization framework outperforms a widely used genetic algorithm while substantially improving computational efficiency, achieving optimization time reductions of 35.3% and 20.8%, respectively. Overall, the proposed MO-DIDC framework provides an effective and computationally efficient tool for the optimization of complex distillation processes.

Sabatier reaction is an emerging strategy for mitigating anthropogenic CO₂ emissions while producing renewable CH₄, a versatile energy carrier for heating, electricity generation, and hydrogen production. Developing efficient, low-cost catalysts is challenging, particularly in achieving stable, well-dispersed supports with tunable surface chemistry for methanation. Biochar derived from agrowastes offers a sustainable alternative to conventional oxide supports owing to its low cost, high carbon content, tunable micro-mesoporosity, and ability to anchor metal nanoparticles. Herein, we design and evaluate date palm trunk biochar-immobilized mono and bimetallic Ni, Fe, and NiM (M = Fe or K) catalysts for CO₂ methanation. With specific objectives of developing a sustainable supported catalyst by pyrolysis at 500 °C, and evaluating the influence of temperature and pressure on using an industrially relevant H₂/CO₂ ratio of 3. CO₂ methanation performance was assessed in a continuous-flow reactor. The biochar exhibited favorable micro-mesoporous characteristics and high carbon content, enabling effective metal dispersion. Ni loading strongly influenced catalytic performance; the optimal catalyst (0.5 mmol Ni g^–1, BCNi-3.0) achieved 58% CO₂ conversion and 91% CH₄ selectivity at 400 °C and 1 bar. Increasing pressure to 30 bar enhanced performance to 76% CO₂ conversion and nearly 99% CH₄ selectivity, with stable operation over 20 h. Bimetallic NiFe catalysts formed NiFe₂O₄ and NiO phases and promoted CO formation via the reverse water gas shift reaction, while K promotion further favored CO production. Overall, biochar-supported Ni-based catalysts demonstrate a sustainable and efficient platform for CO₂ methanation with reduced hydrogen demand.