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Incomplete biomass burning produces considerable amounts of pyrogenic carbonaceous materials (PCMs), which are widely distributed in environmental matrices. Those PCMs undergo different environmental processes and consequently have non-negligible impacts on the global carbon cycle, ecological functions and environmental security. This review provided a comprehensive review of qualitative and quantitative methods, carbon sequestration capabilities and other ecosystem functions of PCMs. In addition, the generation mechanism and environmental health risks of emerging contaminants, especially persistent free radicals (EPFRs) and polycyclic aromatic hydrocarbons (PAHs) associated with PCMs were discussed. The results showed that the coexisting kerogen and coal may interfere with PCMs quantification, and that estimates of PCMs pools vary significantly due to methodological differences, natural variability and limited spatial coverage. The input of PCMs into soils increased soil carbon sequestration through direct carbon contribution and indirect negative priming effect on native SOC. In addition, PCMs can improve soil structure and properties and immobilize/degrade pollutants, which is conducive to the restoration of soil ecology. However, various contaminants associated with PCMs may threaten ecological safety, and thus their formation mechanisms and toxicological pathway to living organisms need to be further investigated. The development of standards for PCMs identification and quantification, application protocols of PCMs in pilot scale, and assessing the effects of PCMs on soil health deserve extended studies. Highlights: • Accurate quantification of PCMs in complex environmental matrix is the key to evaluating their behavior and implication. • PCMs increase SOC via direct carbon input and negative priming effect, which is not well considered in carbon cycling. • The formation and toxicity of emerging pollutants associated with PCMs in real environment need to be evaluated. [ABSTRACT FROM AUTHOR]

Seed oil content is one of the most important quantitative traits in soybean (Glycine max) breeding. Here, we constructed a high-density single nucleotide polymorphism linkage map using two genetically similar parents, Heinong 84 and Kenfeng 17, that differ dramatically in their seed oil contents, and performed quantitative trait loci (QTL) mapping of seed oil content in a recombinant inbred line (RIL) population derived from their cross. We detected five QTL related to seed oil content distributed on five chromosomes. The QTL for seed oil content explained over 10% of the phenotypic variation over two years. This QTL was mapped to an interval containing 20 candidate genes, including a previously reported gene, soybean RING Finger 1a (RNF1a) encoding an E3 ubiquitin ligase. Notably, two short sequences were inserted in the GmRNF1a coding region of KF 17 compared to that of HN 84, resulting in a longer protein variant in KF 17. Our results thus provide information for uncovering the genetic mechanisms determining seed oil content in soybean, as well as identifying an additional QTL and highlighting GmRNF1a as candidate gene for modulating seed oil content in soybean. [ABSTRACT FROM AUTHOR]

Soybean seed appearance quality greatly affects the marketability. The objective of this study was to identify the quantitative trait loci (QTLs) that control the appearance quality of soybean seeds. A total of 256 recombinant inbred lines from Qi Huang No.34 × Ji Dou No.17 were utilized for QTL mapping. We innovatively applied a machine vision system to quantify the seed appearance of each line. As a result of QTL mapping, a total of 145 QTLs for the machine vision parameters were detected across three environments. We integrated QTLs mapped overlapped and obtained 16 QTL hotspots in total. Of these hotspots, hotspot-4–1 was suggested to be a major locus controlling seed size, and hotspot-15 was identified to affect the seed color and texture. The mapping for principal components of the seed appearance also supported it. This study comprehensively dug up the QTLs for seed appearance quality of soybean cultivars while providing an efficient method for phenotyping of seed appearance. These results would contribute to dissecting the genetic bases of seed appearance quality for the improvement of soybean. [ABSTRACT FROM AUTHOR]

As soybean plays an indispensable role in the supply of vegetable oil and protein, balancing the relationship between seed quality and yield traits according to human demand has become an important breeding goal for soybean improvement. Here, 256 intraspecific recombinant inbred lines (RILs), derived from a cross between Qi Huang No.34 (QH34) and Ji Dou No.17 (JD17), were used for quantitative trait loci (QTLs) mapping with remarkable four chemical and physical properties with a purpose for exploring the distribution of excellent alleles in germplasm resources in China. A total of 25 QTLs were detected, of which 10 QTLs inherited the alleles from the parent QH34. Pedigree research on favorable alleles on these QTLs showed the process of excellent alleles pyramided into QH34. Meta-analysis of the 25 QTLs by comparing with existed QTLs in previous study identified 17 novel QTLs. QTLs with pleiotropic effects have been detected. Furthermore, three representative elite recombinant inbred lines in different locations that have great potential in soybean breeding were selected, and finally, four seed weight-related candidate genes were identified. The discovery of these QTLs provides a new guidance for combining the diversity and rarity of germplasm resources, which can effectively increase population genetic diversity and broaden genetic basis of varieties. [ABSTRACT FROM AUTHOR]

Structure plays a vital role in determining materials properties. In lithium ion cathode materials, the crystal structure defines the dimensionality and connectivity of interstitial sites, thus determining lithium ion diffusion kinetics. In most conventional cathode materials that are well-ordered, the average structure as seen in diffraction dictates the lithium ion diffusion pathways. Here, we show that this is not the case in a class of recently discovered high-capacity lithium-excess rocksalts. An average structure picture is no longer satisfactory to understand the performance of such disordered materials. Cation short-range order, hidden in diffraction, is not only ubiquitous in these long-range disordered materials, but fully controls the local and macroscopic environments for lithium ion transport. Our discovery identifies a crucial property that has previously been overlooked and provides guidelines for designing and engineering cation-disordered cathode materials. The average crystal structure largely governs the Li diffusion kinetics in well-ordered cathode materials. Here the authors show this rule does not hold true for cation-disordered analogues. Cation short-range order is not only ubiquitous but also controls the Li transport behavior. [ABSTRACT FROM AUTHOR]

First-principles based cluster expansion models are the dominant approach in ab initio thermodynamics of crystalline mixtures enabling the prediction of phase diagrams and novel ground states. However, despite recent advances, the construction of accurate models still requires a careful and time-consuming manual parameter tuning process for ground-state preservation, since this property is not guaranteed by default. In this paper, we present a systematic and mathematically sound method to obtain cluster expansion models that are guaranteed to preserve the ground states of their reference data. The method builds on the recently introduced compressive sensing paradigm for cluster expansion and employs quadratic programming to impose constraints on the model parameters. The robustness of our methodology is illustrated for two lithium transition metal oxides with relevance for Li-ion battery cathodes, i.e., Li2xFe2(1−x)O2 and Li2xTi2(1−x)O2, for which the construction of cluster expansion models with compressive sensing alone has proven to be challenging. We demonstrate that our method not only guarantees ground-state preservation on the set of reference structures used for the model construction, but also show that out-of-sample ground-state preservation up to relatively large supercell size is achievable through a rapidly converging iterative refinement. This method provides a general tool for building robust, compressed and constrained physical models with predictive power. Materials simulations: Constructing models guaranteed to preserve the ground states A method has been developed for performing materials simulations without needing to perform manual parameter tuning for the ground-state. First-principles density functional theory calculations are one of the most commonly used tools for computational materials science research but they cannot easily be applied to large structures that contain many thousands of atoms. In such systems, cluster expansion models are often used but they have a problem: manual parameter tuning is required to preserve the ground-state --- important as this usually governs the materials properties. An international team of researchers led by Gerbrand Ceder from Massachusetts Institute of Technology, the University of California Berkeley and Lawrence Berkeley National Laboratory now present a procedure for constructing cluster expansion models that can preserve the ground states without any need for tuning. [ABSTRACT FROM AUTHOR]