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He, Zhen

Publications and source records attributed to He, Zhen.

Machine learning-aided inverse design for biogas upgrading through biological CO 2 conversion

The biogas upgrading process through bioconversion of CO 2 to CH 4 by hydrogenotrophic methanogens is an attractive strategy for energy decarbonation. Many studies have optimized operational parameters to improve key performance indicators such as CH 4 % and H 2 utilization efficiency. However, inconsistent laboratory conditions make it challenging to compare results. Existing models for analyzing operating conditions can only assess the impact of individual conditions and lack the ability to simultaneously optimize multiple conditions. To address this, two XGBoost models were built with R 2 of 0.779 and 0.903 with data collected from literatures and were embedded into multi-objective partitive swarm optimization algorithm to optimal operating conditions. Predictions were compared with experimental validations under optimized conditions, revealing an 8.50% and 2.95% relative error in CH 4 % and H 2 conversion rate, respectively. This approach streamlines biogas upgrading processes, offering a data-driven solution to enhance efficiency and consistency in the pursuit of sustainable methane production.

Biogas upgrading↗

Biological upgrading of biogas assisted with membrane supplied hydrogen gas in a three-phase upflow reactor

Biogas upgrading via CO 2 conversion to CH 4 is an emerging technology for renewable natural gas production and carbon management, but its development is limited by the low H 2 gas to liquid phase transfer. Herein, an innovative biogas upgrading system employing a three-phase design was studied for CO 2 conversion with H 2 supply via gas-permeable membrane. The system produced biogas consisted of 74.1 ± 7.1 % CH 4 and 25.9 ± 7.1 % CO 2 with intermittent injection of H 2 . When H 2 supply was continuous, the CH 4 content increased to 91.6 ± 2.2 % at a H 2 :CO 2 ratio of 4.4. Although a higher ratio of 5.5 could result in a higher CH 4 percentage of 95.2 ± 2.5 %, biogas production rate started to decrease. The removal efficiency of organic contents remained above 90 % throughout the experiment. Microbial community analysis corroborated the findings, showing that hydrogenotrophic Methanobacteriaceae was more prevalent in the biofilm (71.9 %) compared to that in anaerobic digestion (15.8 %) and effluent (14.1 %).

Agriculture↗

Meta-analysis of biogas upgrading to renewable natural gas through biological CO 2 conversion

Biogas upgrading through CO 2 conversion by hydrogenotrophic methanogenesis is receiving an increasing attention worldwide because of the demand for renewable natural gas. Herein, a holistic and statistical study of the operation conditions, driving forces, performances, and potential implementation of biogas upgrading via biological CO 2 conversion was conducted. Based on a systematic review and meta-analysis of 46 existing publications that were selected from 1475 papers, we have compiled a global dataset of CO 2 bioconversion biogas upgrading, encompassing 308 study cases. Subsequently, we employed a rigorous analytical framework incorporating data processing and mixed effects linear regression analysis to examine the dataset. This analysis revealed a significant positive relationship between the H 2 :CO 2 ratio and the methane percentage in the upgraded biogas when using the study as a random effect. Furthermore, we performed meta-analysis on observations taken when the ratio was close to 4:1 and found that ex situ reactors (91.93% [88.11%, 95.75%]) can perform better than in situ reactors (84.74% [80.69%, 88.80%]). No evidence of differential performance was found based on the present dataset between different temperature regimes or operation modes. Furthermore, those findings establish a database that will contribute to a deeper understanding of the biogas upgrading via biological CO 2 conversion.

Biogas upgrading↗

(Mg,Mn,Fe,Co,Ni)O: A rocksalt high-entropy oxide containing divalent Mn and Fe

High-entropy oxides (HEOs) have aroused growing interest due to fundamental questions relating to their structure formation, phase stability, and the interplay between configurational disorder and physical and chemical properties. Introducing Fe(ιι) and Mn(ιι) into a rocksalt HEO is considered challenging, as theoretical analysis suggests that they are unstable in this structure under ambient conditions. Here, we develop a bottom-up method for synthesizing Mn- and Fe-containing rocksalt HEO (FeO-HEO). We present a comprehensive investigation of its crystal structure and the random cation-site occupancy. We show the improved structural robustness of this FeO-HEO and verify the viability of an oxygen sublattice as a buffer layer. Compositional analysis reveals the valence and spin state of the iron species. We further report the antiferromagnetic order of this FeO-HEO below the transition temperature ~218 K and predict the conditions of phase stability of Mn- and Fe-containing HEOs. Our results provide fresh insights into the design and property tailoring of emerging classes of HEOs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Reduction-Controlled Atomic Migration for Single Atom Alloy Library

Here, picturing the atomic migration pathways of catalysts in a reactive atmosphere is of central significance for uncovering the underlying catalytic mechanisms and directing the design of high-performance catalysts. Here, we describe a reduction-controlled atomic migration pathway that converts nanoparticles to single atom alloys (SAAs), which has remained synthetically challenging in prior attempts due to the elusive mechanism. We achieved this by thermally treating the noble-metal nanoparticles M (M = Ru, Rh, Pd, Ag, Ir, Pt, and Au) on metal oxide (CuO) supports with H2/Ar. Atomic-level characterization revealed such conversion as the synergistic consequence of noble metal-promoted H2 dissociation and concomitant CuO reduction. The observed atomic migration pathway offers an understanding of the dynamic mechanisms study of nanomaterials formation and catalyst design.

36 MATERIALS SCIENCE↗