Multiple Metal–Nitrogen Bonds Synergistically Boosting the Activity and Durability of High-Entropy Alloy Electrocatalysts
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36 MATERIALS SCIENCE↗
Engineering topics
Cheng, Hao
Publications and source records attributed to Cheng, Hao.
Microbiome-enabled genomic selection improves prediction accuracy for nitrogen-related traits in maize
Root-associated microbiomes in the rhizosphere (rhizobiomes) are increasingly known to play an important role in nutrient acquisition, stress tolerance, and disease resistance of plants. However, it remains largely unclear to what extent these rhizobiomes contribute to trait variation for different genotypes and if their inclusion in the genomic selection protocol can enhance prediction accuracy. To address these questions, we developed a microbiome-enabled genomic selection method that incorporated host SNPs and amplicon sequence variants from plant rhizobiomes in a maize diversity panel under high and low nitrogen (N) field conditions. Our cross-validation results showed that the microbiome-enabled genomic selection model significantly outperformed the conventional genomic selection model for nearly all time-series traits related to plant growth and N responses, with an average relative improvement of 3.7%. The improvement was more pronounced under low N conditions (8.4–40.2% of relative improvement), consistent with the view that some beneficial microbes can enhance N nutrient uptake, particularly in low N fields. However, our study could not definitively rule out the possibility that the observed improvement is partially due to the amplicon sequence variants being influenced by microenvironments. Using a high-dimensional mediation analysis method, our study has also identified microbial mediators that establish a link between plant genotype and phenotype. Some of the detected mediator microbes were previously reported to promote plant growth. The enhanced prediction accuracy of the microbiome-enabled genomic selection models, demonstrated in a single environment, serves as a proof-of-concept for the potential application of microbiome-enabled plant breeding for sustainable agriculture.
60 APPLIED LIFE SCIENCES↗
Divergent accumulation of amino sugars and lignins mediated by soil functional carbon pools under tropical forest conversion
Tropical primary forests are being destroyed at an alarming rate and converted for other land uses which is expected to greatly influence soil carbon (C) cycling. However, our understanding of how tropical forest conversions affect the accumulation of compounds in soil functional C pools remains unclear. Here, we collected soils from primary forests (PF), secondary forests (SF), oil-palm (OP), and rubber plantations (RP), and assessed the accumulation of plant- and microbial-derived compounds within soil organic carbon (SOC), particulate (POC) and mineral-associated (MAOC) organic C. PF conversion to RP greatly decreased SOC, POC, and MAOC concentrations, whereas conversion to SF increased POC concentrations and decreased MAOC concentrations, and conversion to OP only increased POC concentrations. PF conversion to RP decreased lignin concentrations and increased amino sugar concentrations in SOC pools which increased the stability of SOC, whereas conversion to SF only increased the lignin concentrations in POC, and conversion to OP just increased lignin concentrations in POC and decreased it in MAOC. We observed divergent dynamics of amino sugars (decrease) and lignin (increase) in SOC with increasing SOC. Only lignin concentrations increased in POC with increasing POC and amino sugars concentrations decreased in MAOC with increasing MAOC. Conversion to RP significantly decreased soil enzyme activities and microbial biomasses. Lignin accumulation was associated with microbial properties, whereas amino sugar accumulation was mainly associated with soil nutrients and stoichiometries. These results suggest that the divergent accumulation of plant- and microbial-derived C in SOC was delivered by the distribution and original composition of functional C pools under forest conversions. Forest conversions changed the formation and stabilization processes of SOC in the long run which was associated with converted plantations and management. As a result, the important roles of soil nutrients and stoichiometry also provide a natural-based solution to enhance SOC sequestration via nutrient management in tropical forests.
59 BASIC BIOLOGICAL SCIENCES↗
A single-atom library for guided monometallic and concentration-complex multimetallic designs
Atomically dispersed single-atom catalysts have the potential to bridge heterogeneous and homogeneous catalysis. Dozens of single-atom catalysts have been developed, and they exhibit notable catalytic activity and selectivity that are not achievable on metal surfaces. Although promising, there is limited knowledge about the boundaries for the monometallic single-atom phase space, not to mention multimetallic phase spaces. Here, single-atom catalysts based on 37 monometallic elements are synthesized using a dissolution-and-carbonization method, characterized and analyzed to build the largest reported library of single-atom catalysts. In conjunction with in situ studies, we uncover unified principles on the oxidation state, coordination number, bond length, coordination element and metal loading of single atoms to guide the design of single-atom catalysts with atomically dispersed atoms anchored on N-doped carbon. We utilize the library to open up complex multimetallic phase spaces for single-atom catalysts and demonstrate that there is no fundamental limit on using single-atom anchor sites as structural units to assemble concentration-complex single-atom catalyst materials with up to 12 different elements. Furthermore, our work offers a single-atom library spanning from monometallic to concentration-complex multimetallic materials for the rational design of single-atom catalysts.
36 MATERIALS SCIENCE↗
Objective Phenotyping of Root System Architecture Using Image Augmentation and Machine Learning in Alfalfa (Medicago sativa L.)
Active breeding programs specifically for root system architecture (RSA) phenotypes remain rare; however, breeding for branch and taproot types in the perennial crop alfalfa is ongoing. Phenotyping in this and other crops for active RSA breeding has mostly used visual scoring of specific traits or subjective classification into different root types. While image-based methods have been developed, translation to applied breeding is limited. This research is aimed at developing and comparing image-based RSA phenotyping methods using machine and deep learning algorithms for objective classification of 617 root images from mature alfalfa plants collected from the field to support the ongoing breeding efforts. Our results show that unsupervised machine learning tends to incorrectly classify roots into a normal distribution with most lines predicted as the intermediate root type. Encouragingly, random forest and TensorFlow-based neural networks can classify the root types into branch-type, taproot-type, and an intermediate taproot-branch type with 86% accuracy. With image augmentation, the prediction accuracy was improved to 97%. Coupling the predicted root type with its prediction probability will give breeders a confidence level for better decisions to advance the best and exclude the worst lines from their breeding program. This machine and deep learning approach enables accurate classification of the RSA phenotypes for genomic breeding of climate-resilient alfalfa.
59 BASIC BIOLOGICAL SCIENCES↗