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Control of bubble ζ-potentials to improve the kinetics of bubble-particle interactions
Not provided.
Improved Annotation of Untargeted Metabolomics Data through Buffer Modifications That Shift Adduct Mass and Intensity
Annotation of untargeted high-resolution full-scan LC-MS metabolomics data remains challenging due to individual metabolites generating multiple LC-MS peaks arising from isotopes, adducts and fragments. Adduct annotation is a particular challenge, as the same mass difference between peaks can arise from adduct formation, fragmentation, or different biological species. To address this, here we describe a Buffer Modification Workflow (BMW), in which the same sample is run by LC-MS in both liquid chromatography solvent with 14 NH 3 -acetate buffer, and in solvent with the buffer modified with 15 NH 3 -formate. Buffer switching results in characteristic mass and signal intensity changes for adduct peaks, facilitating their annotation. This relatively simple and convenient chromatography modification annotated yeast metabolomics data with similar effectiveness to growing the yeast in isotope-labeled media. Application to mouse liver data annotated both known metabolite and known adduct peaks with 95% accuracy. Altogether, it identified 26% of ~ 27,000 liver LC-MS features as putative metabolites, of which ~ 2600 showed HMDB or KEGG database formula match. This workflow is well-suited to biological samples that cannot be readily isotope labeled, including plants, mammalian tissues, and tumors.
Vapor-Fed Cathode Microbial Electrolysis Cells with Closely Spaced Electrodes Enables Greatly Improved Performance
Hydrogen can be electrochemically produced in microbial electrolysis cells (MECs) by current generated from bacterial anodes with a small added voltage. MECs typically use a liquid catholyte containing a buffer or salts. However, anions in these catholytes result in charge being balanced predominantly by ions other than hydroxide or protons, leading to anode acidification. To enhance only hydroxide ion transport to the anode, we developed a novel vapor-fed MEC configuration lacking a catholyte with closely spaced electrodes and an anion exchange membrane to limit the acidification. This MEC design produced a record-high sustained current density of 43.1 ± 0.6 A/m 2 and a H 2 production rate of 72 ± 2 LH 2 /L-d (cell voltage of 0.79 ± 0.00 V). There was minimal impact on MEC performance of increased acetate concentrations, solution conductivity, or anolyte buffer capacity at applied voltages up to 1.1 V, as shown by a nearly constant internal resistance of only 6.8 ± 0.3 mΩ m 2 . At applied external voltages >1.1 V, the buffer capacity impacted performance, with current densities increasing from 28.5 ± 0.6 A/m 2 (20 mM phosphate buffer solution (PBS)) to 51 ± 1 A/m 2 (100 mM PBS). Furthermore, these results show that a vapor-fed MEC can produce higher and more stable performance than liquid-fed cathodes by enhancing transport of hydroxide ions to the anode.
High-Level Reverse Intersystem Crossing and Molecular Rigidity Improve Spin Statistics for Triplet–Triplet Annihilation Upconversion
The structural factors affecting triplet−triplet annihilation (TTA) at the molecular level are not well-understood. Here, our steady-state photoluminescence and transient absorption results demonstrate that the spin statistical factor, η, decreases from 0.60 to 0.46 and 0.14 going from 9,10-diphenylanthracene (DPA) to the 1,5-DPA and 2,6-DPA isomers, respectively, during photon upconversion with a platinum octaethylporphyrin sensitizer. Density functional theory (DFT) shows that η depends on the energetics of hot triplet states and molecular rigidity. The significantly high conical intersection energy between the S 0 and T 1 states for 9,10-DPA gives its longer triplet lifetime. Time-dependent DFT calculations show that 9,10-DPA and 1,5-DPA can undergo high-level reverse intersystem crossing from their T 2 and T 3 states, respectively, to the bright S 1 state, increasing the limit of the spin statistical factor. Both factors ultimately serve to enhance the TTA efficiency. Furthermore, this work provides insight into designing molecules for efficient light-emitting and photon upconversion applications.
Electrochemical Modeling of GITT Measurements for Improved Solid-State Diffusion Coefficient Evaluation
Not Available
Improving Lithium Metal Composite Anodes with Seeding and Pillaring Effects of Silicon Nanoparticles
Metallic lithium (Li) anodes are crucial for the development of high specific energy batteries yet plagued by their poor cycling efficiency. Electrode architecture engineering is vital for maintaining a stable anode volume and suppress Li corrosion during cycling. In this paper, a reduced graphene oxide “host” framework for Li metal anodes is further optimized by embedding silicon (Si) nanoparticles between the graphene layers. They serve as Li nucleation seeds to promote Li deposition within the framework even without pre-stored Li. Meanwhile, the LixSi alloy particles serve as supporting “pillars” between the graphene layers, enabling a minimized thickness shrinkage after full stripping of metallic Li. Combined with a Li compatible electrolyte, 99.4% Coulombic efficiency over ~600 cycles is achieved, and stable cycling of a Li||NMC532 full cell for ~380 cycles with negligible capacity decay is realized.
All-perovskite tandem solar cells with improved grain surface passivation
All-perovskite tandem solar cells hold the promise of surpassing the efficiency limits of single-junction solar cells; however, until now, the best-performing all-perovskite tandem solar cells have exhibited lower certified efficiency than have single-junction perovskite solar cells. A thick mixed Pb–Sn narrow-bandgap subcell is needed to achieve high photocurrent density in tandem solar cells, yet this is challenging owing to the short carrier diffusion length within Pb–Sn perovskites. Here we develop ammonium-cation-passivated Pb–Sn perovskites with long diffusion lengths, enabling subcells that have an absorber thickness of approximately 1.2 μm. Molecular dynamics simulations indicate that widely used phenethylammonium cations are only partially adsorbed on the surface defective sites at perovskite crystallization temperatures. The passivator adsorption is predicted to be enhanced using 4-trifluoromethyl-phenylammonium (CF3-PA), which exhibits a stronger perovskite surface-passivator interaction than does phenethylammonium. By adding a small amount of CF3-PA into the precursor solution, we increase the carrier diffusion length within Pb–Sn perovskites twofold, to over 5 μm, and increase the efficiency of Pb–Sn perovskite solar cells to over 22%. Here, we report a certified efficiency of 26.4% in all-perovskite tandem solar cells, which exceeds that of the best-performing single-junction perovskite solar cells. Encapsulated tandem devices retain more than 90% of their initial performance after 600 h of operation at the maximum power point under 1 Sun illumination in ambient conditions.
Structural analyses of NudT16-ADP-ribose complexes direct rational design of mutants with improved processing of poly(ADP-ribosyl)ated proteins
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A hole-transport material that also passivates perovskite surface defects for solar cells with improved efficiency and stability
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Improved slit-shaped microseparator and its integration with a microreactor for modular biomanufacturing
Pictures of microseparator parts and integrated microreactor and microseparator to produce high purity streams containing platform biomass-derived chemicals.
Improving nuclear data evaluations with predictive reaction theory and indirect measurements
Nuclear reaction data required for astrophysics and applications is incomplete, as not all nuclear reactions can be measured or reliably predicted. Neutron-induced reactions involving unstable targets are particularly challenging, but often critical for simulations. In response to this need, indirect approaches, such as the surrogate reaction method, have been developed. Nuclear theory is key to extract reliable cross sections from such indirect measurements. We describe ongoing efforts to expand the theoretical capabilities that enable surrogate reaction measurements. We focus on microscopic predictions for charged-particle inelastic scattering, uncertainty-quantified optical nucleon-nucleus models, and neural-network enhanced parameter inference.
Machine learning improves predictions of agricultural nitrous oxide (N 2 O) emissions from intensively managed cropping systems
Not provided.
Improving streamflow predictions across CONUS by integrating advanced machine learning models and diverse data
Accurate streamflow prediction is crucial to understand climate impacts on water resources and develop effective adaption strategies. A global long short-term memory (LSTM) model, using data from multiple basins, can enhance streamflow prediction, yet acquiring detailed basin attributes remains a challenge. To overcome this, we introduce the Geo-vision transformer (ViT)-LSTM model, a novel approach that enriches LSTM predictions by integrating basin attributes derived from remote sensing with a ViT architecture. Applied to 531 basins across the Contiguous United States, our method demonstrated superior prediction accuracy in both temporal and spatiotemporal extrapolation scenarios. Geo-ViT-LSTM marks a significant advancement in land surface modeling, providing a more comprehensive and effective tool for better understanding the environment responses to climate change.
Deletion of biofilm synthesis in Eubacterium limosum ATCC 8486 improves handling and transformation efficiency
Abstract Eubacterium limosum is an acetogenic bacterium of potential industrial relevance for its ability to efficiently metabolize a range of single carbon compounds. However, extracellular polymeric substance (EPS) produced by the type strain ATCC 8486 is a serious impediment to bioprocessing and genetic engineering. To remove these barriers, here we bioinformatically identified genes involved in EPS biosynthesis, and targeted several of the most promising candidates for inactivation, using a homologous recombination-based approach. Deletion of a single genomic region encoding homologues for epsABC, ptkA, and tmkA resulted in a strain incapable of producing EPS. This strain is significantly easier to handle by pipetting and centrifugation, and retains important wild-type phenotypes including the ability to grow on methanol and carbon dioxide and limited oxygen tolerance. Additionally, this strain is also more genetically tractable with a 2-fold increase in transformation efficiency compared to the highest previous reports. This work advances a simple, rapid protocol for gene knockouts in E. limosum using only the native homologous recombination machinery. These results will hasten the development of this organism as a workhorse for valorization of single carbon substrates, as well as facilitate exploration of its role in the human gut microbiota.
Improved Decarbonization Planning through Climate Resiliency Modeling
California has set ambitious decarbonization goals concerning both emissions and the portion of energy generated by renewable resources. However, climate change poses considerable uncertainties. Variation in load is driven both by the level of electrification as well as the impact of climate change on weather. Further, climate change stands to make weather more variable, impacting not just load but generation from renewable resources. In this paper, we approach the issue of the impacts of climate change on decarbonization planning from two perspectives. First, we look at the range of decarbonization pathways through 8 pathways that account for differences in socioeconomic development, global emissions, and warming. Second, we develop a more robust way of ensuring the reliability of energy resources planning than the commonly used planning reserve margin. We show that the proposed method can save between 6 and 14 billion dollars in investment and maintenance costs and outline critical policy implications concerning the reliance of power plants for satisfying planning reliability requirements, including the potential retirement of dozens of peaker power plants.
Improving Frequency Stability and Minimizing Load Shedding Events by Adopting Grid-Scale Energy Storage with Grid Forming Inverters
The upward adoption trend of renewable generation not only means cleaner energy integrated into modern power grids, but also that most new generation sources are based on front-end inverter bridges, used as interfaces to most wind generation and all the solar PV. It is well known that due to their power electronics-based construction rather than rotational shafts, these sources do not provide inertia inherently, nor substantial amounts of short-circuit currents. However, stable energy such as what can be stored in energy storage systems, although interfaced via inverters, can be controlled to respond to system disturbances in a manner that emulates inertial behavior. This paper focuses on the application of such energy storage systems to augment inertia in the island of Puerto Rico. To do so, a user defined inverter model that contains grid forming capabilities and fast frequency response is modeled and integrated into the real transmission system in power flow and dynamics software. Energy storage is then connected to two selected areas so that it not only provides frequency regulation to avoid widespread load shedding events, but also other tangible benefits. The simulated cases suggest that even relatively small energy storage systems can avert load shedding events if adequately placed in the transmission network.