Disparities and mechanisms of carbon and nitrogen conversion during food waste composting with different bulking agents.

The low C/N ratio, high moisture content, and low porosity of food waste require the addition of bulking agents for adjustment during the composting process. However, the effect and mechanism of different bulking agents on the reduction of carbon and nitrogen losses are unclear. Therefore, this study conducted experiments to evaluate and clarify the differences in carbon and nitrogen transformation between sawdust, rice husk and wheat bran in food waste composting. The results showed that the addition of bulking agents promoted the conversion of carbon and nitrogen into total organic carbon (TOC) and total organic nitrogen (TON) rather than CO2 and NH3. The carbon and nitrogen losses were reduced by 16.00-25.71% and 11.56-29.54%, respectively. Notably, the Sawdust group exhibited the highest carbon retention, whereas the Wheat_bran group demonstrated superior nitrogen retention. The succession of bacterial communities showed that sawdust enhanced the cellulolysis and xylanolysis functions while wheat bran promoted nitrogen fixation. Correlation analysis was further employed to speculate on potential interactions among carbon and nitrogen components. The incorporation of sawdust and rice husk improved humification partly due to the addition of lignocellulose and the accumulation of total dissolved nitrogen (DTN) in the substrate, respectively. In the process of ammonia assimilation, the addition of wheat bran promoted the accumulation of dissolved organic carbon (DOC), contributing to the synthesis of TON to a degree. These findings offer cost-effective strategies for conserving carbon and nitrogen from loss in food waste composting by selecting suitable bulking agents, ultimately producing high-quality fertilizer.

Microbiome data analysis via machine learning models: Exploring vital players to optimize kitchen waste composting system.

Composting, reliant on microorganisms, effectively treats kitchen waste. However, it is difficult to precisely understand the specific role of key microorganisms in the composting process by relying solely on experimental research. This study aims to employ machine learning models to explore key microbial genera and to optimize composting systems. After introducing a novel microbiome preprocessing approach, Stacking models were constructed (R2 is about 0.8). The SHAP method (SHapley Additive exPlanations) identified Bacillus, Acinetobacter, Thermobacillus, Pseudomonas, Psychrobacter, and Thermobifida as prominent microbial genera (Shapley values ranging from 3.84 to 1.24). Additionally, microbial agents were prepared to target the identified key genera, and experiments demonstrated that the composting quality score was 76.06 for the treatment and 70.96 for the control. The exogenous agents enhanced decomposition and improved compost quality in later stages. In summary, this study opens up a new avenue to identifying key microorganisms and optimizing the biological treatment process.

Improving kitchen waste composting maturity by optimizing the processing parameters based on machine learning model.

As a novel analytical method based on big data, machine learning model can explore the relationship between different parameters and draw universal conclusions, which was used to predict composting maturity and identify key parameters in this study. The results showed that the Stacking model exhibited excellent prediction accuracy. SHapley Additive exPlanations (SHAP) and Partial Dependence Analysis (PDA) were performed to evaluate the importance of different parameters as well as their optimal interval. Optimal starting conditions should be maintained in the mesophilic state (temperature: 30-45℃, moisture content: 55-65%, pH: 6.3-8.0), and nutrients (total nitrogen > 2.3%, total organic carbon > 35%) should be adjusted in the thermophilic state. Experiments revealed that model-based optimization strategies could improve composting maturity because they could optimize compost microbial flora and perform complex carbon cycle functions. In conclusion, this study provides new insights into the enhancement of the composting process.

Enabling Superhydrophobicity-Guided Superwicking in Metal Alloys via a Nanosecond Laser-Based Surface Treatment Method.

Enabling capillary wicking on bulk metal alloys is challenging due to processing complexity at different size scales. This work presents a laser-chemical surface treatment to fabricate superwicking patterns guided by a superhydrophobic region over a large-area metal alloy surface. The laser-chemical surface treatment generates surface micro/nanostructures and desirable surface chemistry simultaneously. The superhydrophobic surface was first fabricated over the whole surface by laser treatment under water confinement and fluorosilane treatment; subsequently, superwicking stripes were processed by a second laser treatment in air and cyanosilane treatment. The resultant surface shows superwicking regions surrounded by superhydrophobic regions. During the process, superwicking regions possess dual-scale structures and polar nitrile surface chemistry. In contrast, random nanoscale structures and fluorocarbon chemistry are generated on the superhydrophobic region of the aluminum alloy 6061 substrates. The resultant superwicking region demonstrates self-propelling anti-gravity liquid transport for methanol and water. The combination of the capillary effect of the dual-scale surface microgrooves and the water affinitive nitrile group contributes toward the self-propelling movement of water and methanol at the superwicking region. The initial phase of wicking followed Washburn dynamics, whereas it entered a non-linear regime in the later phase. The wicking height and rate are regulated by microgroove geometry and spacing.

Effect of silver electrode wetting state on oxygen reduction electrochemistry.

Surface wettability plays an important role in heterogeneous electrocatalysis. Here we report a facile laser ablation strategy to directly modify the wettability of the silver catalyst surface and investigate its effect on oxygen reduction reaction (ORR). A broad range tuning of 2e-/4e- ORR pathways was achieved, with hydrophilic silver surfaces (contact angle (θw) 31.1°± 0.6°) showing high activity and selectivity towards 4e- reduction of oxygen to water.

Design of Chemical Surface Treatment for Laser-Textured Metal Alloys to Achieve Extreme Wetting Behavior.

Extreme wetting activities of laser-textured metal alloys have received significant interest due to their superior performance in a wide range of commercial applications and fundamental research studies. Fundamentally, extreme wettability of structured metal alloys depends on both the surface structure and surface chemistry. However, compared with the generation of physical topology on the surface, the role of surface chemistry is less explored for the laser texturing processes of metal alloys to tune the wettability. This work introduces a systematic design approach to modify the surface chemistry of laser textured metal alloys to achieve various extreme wettabilities, including superhydrophobicity/superoleophobicity, superhydrophilicity/superoleophilicity, and coexistence of superoleophobicity and superhydrophilicity. Microscale trenches are first created on the aluminum alloy 6061 surfaces by nanosecond pulse laser surface texturing. Subsequently, the textured surface is immersion-treated in several chemical solutions to attach target functional groups on the surface to achieve the final extreme wettability. Anchoring fluorinated groups (-CF2- and -CF3) with very low dispersive and nondispersive surface energy leads to superoleophobicity and superhydrophobicity, resulting in repelling both water and diiodomethane. Attachment of the polar nitrile (-C≡N) group with very high nondispersive and high dispersive surface energy achieves superhydrophilicity and superoleophilicity by drawing water and diiodomethane molecules in the laser-textured capillaries. At last, anchoring fluorinated groups (-CF2- and -CF3) and polar sodium carboxylate (-COONa) together leads to very low dispersive and very high nondispersive surface energy components. It results in the coexistence of superoleophobicity and superhydrophilicity, where the treated surface attracts water but repels diiodomethane.