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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Correction to “Disentangling the History of Deep Ocean Disposal for DDT and Other Industrial Waste Off Southern California”
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Correction to “Greenhouse Gas and Air Pollutant Emissions from Composting”
Composting can divert organic waste from landfills, reduce landfill methane emissions, and recycle nutrients back to soils. However, the composting process is also a source of greenhouse gas and air pollutant emissions. Researchers, regulators, and policy decision-makers all rely on emissions estimates to develop local emissions inventories and weigh competing waste diversion options, yet reported emission factors are difficult to interpret and highly variable. This review explores the impacts of waste characteristics, pretreatment processes, and composting conditions on CO 2 , CH 4 , N 2 O, NH 3 , and VOC emissions by critically reviewing and analyzing 388 emission factors from 46 studies. The values reported to date suggest that CH 4 is the single largest contributor to 100-year global warming potential (GWP100) for yard waste composting, comprising approximately 80% of the total GWP 100 . For nitrogen-rich wastes including manure, mixed municipal organic waste, and wastewater treatment sludge, N 2 O is the largest contributor to GWP 100 , accounting for half to as much as 90% of the total GWP 100 . If waste is anaerobically digested prior to composting, N 2 O, NH 3 , and VOC emissions tend to decrease relative to composting the untreated waste. Effective pile management and aeration are key to minimizing CH 4 emissions. However, forced aeration can increase NH 3 emissions in some cases.
Correction for “Structure–Transport Relationships of Water–Organic Solvent Co-transport in Carbon Molecular Sieve (CMS) Membranes”
There were two important typos identified in the reported units of water flux under reverse osmosis conditions.
Correction to Standard Reduction Potentials for Oxygen and Carbon Dioxide Couples in Acetonitrile and N , N -Dimethylformamide
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Correction to “A Practical Beginner’s Guide to Cyclic Voltammetry”
In the Supporting Information, the solutions to Training Module 3, Questions 1, 3, and 4 have been revised to enhance clarity and include details that were omitted in the original submission.
Study of the Decoherence Correction Derived from the Exact Factorization Approach for Nonadiabatic Dynamics
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Accurate Free Energies for Complex Condensed-Phase Reactions Using an Artificial Neural Network Corrected DFTB/MM Methodology
Semiempirical methods like density functional tight-binding (DFTB) allow extensive phase space sampling, making it possible to generate free energy surfaces of complex reactions in condensed-phase environments. Such a high efficiency often comes at the cost of reduced accuracy, which may be improved by developing a specific reaction parametrization (SRP) for the particular molecular system. Thiol–disulfide exchange is a nucleophilic substitution reaction that occurs in a large class of proteins. Its proper description requires a high-level ab initio method, while DFT-GAA and hybrid functionals were shown to be inadequate, and so is DFTB due to its DFT-GGA descent. We develop an SRP for thiol–disulfide exchange based on an artificial neural network (ANN) implementation in the DFTB+ software and compare its performance to that of a standard SRP approach applied to DFTB. Furthermore, as an application, we use both new DFTB-SRP as components of a QM/MM scheme to investigate thiol–disulfide exchange in two molecular complexes: a solvated model system and a blood protein. Demonstrating the strengths of the methodology, highly accurate free energy surfaces are generated at a low cost, as the augmentation of DFTB with an ANN only adds a small computational overhead.
An Ab Initio Correction Vector Restricted Active Space Approach to the L-Edge XAS and 2p3d RIXS Spectra of Transition Metal Complexes
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MLTB: Enhancing Transferability and Extensibility of Density Functional Tight-Binding Theory with Many-body Interaction Corrections
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Correction to “COCOMO2: A Coarse-Grained Model for Interacting Folded and Disordered Proteins”
Biomolecular interactions are essential in many biological processes, including complex formation and phase separation processes. Coarse-grained computational models are especially valuable for studying such processes via simulation. Here, we present COCOMO2, an updated residue-based coarse-grained model that extends its applicability from intrinsically disordered peptides to folded proteins. This is accomplished with the introduction of a surface exposure scaling factor, which adjusts interaction strengths based on solvent accessibility, to enable the more realistic modeling of interactions involving folded domains without additional computational costs. COCOMO2 was parametrized directly with solubility and phase separation data to improve its performance on predicting concentration-dependent phase separation for a broader range of biomolecular systems compared to the original version. COCOMO2 enables new applications including the study of condensates that involve IDPs together with folded domains and the study of complex assembly processes. COCOMO2 also provides an expanded foundation for the development of multiscale approaches for modeling biomolecular interactions that span from residue-level to atomistic resolution.