Carbon dioxide fixation presents a potential solution for mitigating the greenhouse gas issue. During carbon dioxide fixation, C1 compound reduction requires a high energy supply. Thermodynamic calculations suggest that the energy source for cofactor regeneration plays a vital role in the effective enzymatic C1 reduction. Hydrogenase utilizes renewable hydrogen to achieve the regeneration and supply cofactor nicotinamide adenine dinucleotide (NADH), providing a driving force for the reduction reaction to reduce the thermodynamic barrier of the reaction cascade, and making the forward reduction pathway thermodynamically feasible. Based on the regeneration of cofactor NADH by hydrogenase, and coupled with formaldehyde dehydrogenase and formolase, a favorable thermodynamic mode of the C1 reduction pathway for reducing formate to dihydroxyacetone (DHA) was designed and constructed. This resulted in accumulation of 373.19 μmol·L^–1 DHA after 2 h, and conversion reaching 7.47%. These results indicate that enzymatic utilization of hydrogen as the electron donor to regenerate NADH is of great significance to the sustainable and green development of bio-manufacturing because of its high economic efficiency, no by-products, and environment-friendly operation. Moreover, formolase efficiently and selectively fixed the intermediate formaldehyde (FALD) to DHA, thermodynamically pulled formate to efficiently reduce to DHA, and finally stored the low-grade renewable energy into chemical energy with high energy density.

Photothermal catalytic oxidation emerges as a promising method for the removal of volatile organic compounds (VOCs). Herein, via sol-gel impregnation method, spinel CuMn₂O₄ was coated on attapulgite honeycombs with integrating biochar (BC) film as the second carrier, using chestnut shell as complexation agent. Various mass ratios of CuMn₂O₄ to chestnut shell was modulated to investigate the catalytic toluene degradation performance. Results indicated that the monolithic CuMn₂O₄/BC/honeycomb catalyst demonstrated superior photothermal catalytic toluene degradation with a low T₉₀ (temperature at 90% degradation) of 263 °C when the mass ratio of CuMn₂O₄ to biomass was 1:4. The addition of BC film substantially increased the honeycomb's specific surface area and improved the photothermal conversion of spinel, leading to enhanced photothermal catalytic activity. This study presents a cost-effective strategy for eliminating industrial VOCs using clay-biomass based monolithic catalyst.

Amino acids are important nitrogen carriers in biomass and entail a broad spectrum of industrial uses, most notably as food additives, pharmaceutical ingredients, and raw materials for bio-based plastics. Attaining detailed information in regard to the fragmentation of amino acids is essential to comprehend the nitrogen transformation chemistry at conditions encountered during hydrothermal and pyrolytic degradation of biomass. The underlying aim of this review is to survey literature studies pertinent to the complex structures of amino acids, their formation yields from various categories of biomass, and their fragmentation routes at elevated temperatures and in the aqueous media. Two predominant degradation reactions ensue in the decomposition of amino acids, namely deamination and decarboxylation. Notably, minor differences in structure can greatly affect the fate for each amino acid. Moreover, amino acids, being nitrogen-rich compounds, play pivotal roles across various fields. There is a growing interest in producing varied types and configurations of amino acids. Microbial fermentation appears to a viable approach to produce amino acids at an industrial scale. One innovative method under investigation is catalytic synthesis using renewable biomass as feedstocks. Such a method taps into the inherent nitrogen in biomass sources like chitin and proteins, eliminating the need for external nitrogen sources. This environmentally friendly approach is in line with green chemistry principles and has been gathering momentum in the scientific community.

Microbial lipid fermentation encompasses intricate complex cell growth processes and heavily relies on expert experience for optimal production. Digital modeling of the fermentation process assists researchers in making intelligent decisions, employing logical reasoning and strategic planning to optimize lipid fermentation. It this study, the effects of medium components and concentrations on lipid fermentation were investigated, first. And then, leveraging the collated data, a variety of machine learning algorithms were used to model and optimize the lipid fermentation process. The models, based on artificial neural networks and support vector machines, achieved R² values all higher than 0.93, ensuring accurate predictions of the fermentation process. Multiple linear regression was used to evaluate the respective target parameter, which were affected by the medium components of lipid fermentation. Lastly, single and multi-objective optimization were conducted for lipid fermentation using the genetic algorithm. Experimental results demonstrated the maximum biomass of 50.3 g·L⁻¹ and maximum lipid concentration of 14.1 g·L⁻¹ with the error between the experimental and predicted values less than 5%. The results of the multi-objective optimization reveal the synergistic and competitive relationship between biomass, lipid concentration, and conversion rate, which lay a basis for in-depth optimization and amplification.