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The geochemical cycling of iron and carbon can couple in unique ways in the euphotic zone of aquatic systems.
Our objective is to apply machine learning (ML) algorithms for the prediction of molecular catalysis descriptors from geometric properties derived from experimental crystallographic databases. Catalysis is often considered a “low-data” discipline that is poorly suited for ML methods. An exception is the extensive structural information that is available for molecular catalysts through the Cambridge Structural Database (CSD), which contains atomically precise molecular structures from X-ray diffraction analysis for >600K metal complexes. As a proof-of-principle, we targeted the prediction of hydricity, a thermodynamic property that provides understanding and control of catalytic hydride transfer. We built a training set composed of ~100 molecular complexes with a known hydricity and structural information from the CSD. This data set was converted into a machine-readable format using the smooth overlap of atomic positions (SOAP) representation and further labeled with simple electronic descriptors for the metal centers. Multiple different neural networks were trained on this data set, and the accuracy of the hydricity predictions ranged from < 2 kcal/mol to 20 kcal/mol. The accuracy of each model was highly sensitive to which compounds were in the train versus test set, underscoring the challenges associated with small and chemically diverse data sets. Finally, to further augment the data set, we attempted to experimentally measure several new hydricity values, however these experiments were unsuccessful due to undesired chemical reactivity of the selected complexes.
Recent results on meteor ablation are used as input to a model in which ablation (or evaporation) is the source and deposition on dust particles is the sink. The dust comes from recondensation of the rest of the meteor vapor. An excellent match is found to the observed characteristics of the sodium layer. It is argued that the high-latitude winter maximum of abundance (and layer height) is due to the much lower rate of ionization there and over the polar cap. The computations show the required factor of 4-5 and also match the height variation. To keep the ion density from being too large, downward electrodynamic transport at about 50 cm/sec must be invoked. The different behavior and low abundance of potassium require an additional sink. The one proposed is Penning ionization by metastable excited O2. Inherent in the model is that sodium and its compounds reside almost entirely on dust particles below 80 km. Large abundances in the free form at stratospheric heights are very unlikely and are not a good explanation of observed ambient ions.
Dynamic mechanical analysis was performed on a variety of temperature resistant polymers and composite resin matrices. Data on glass transition temperatures and degree of cure attained were derived. In addition a laboratory based computer system was installed and data base set up to allow entry of composite data. The laboratory CPU termed TYCHO is based on a DEC PDP 11/44 CPU with a Datatrieve relational data base. The function of TYCHO is integration of chemical laboratory analytical instrumentation and storage of chemical structures for modeling of new polymeric structures and compounds
A time-dependent box model of the lower troposphere which includes a description of photochemical and physical processes has been developed. This model has been applied to the calculation of nitric acid and NO(x)(NO + NO2) concentrations over a diurnal cycle which includes precipitation. Nitric acid concentrations and the HNO3/NO(x) ratio are found to be highly variable under the assumptions regarding the frequency, duration, and intensity of precipitation employed in this model. The chemistry of odd nitrogen compounds during the night is potentially important in establishing the level of nitric acid in the lower troposphere. These calculations also indicate that relatively large errors may occur when the continuity equation describing nitric acid variations is averaged over a diurnal cycle which includes precipitation. Interpretation of simultaneous measurements of HNO3 and NO(x) will require some knowledge of the history of the observed air mass and may require an improved understanding of nighttime odd nitrogen chemistry.
Protonated formaldehyde and protonated methanol are candidate interstellar molecules and models for classes of protonated oxygen compounds. Ab initio molecular orbital theory has been used to compute rotational constants to guide spectroscopic searches both in the laboratory and in space. The ab initio results are empirically correct to account for systematic deficiencies in the theory and zero-point vibrational effects; they are expected to be accurate to about + or - 2 percent. For H2COH(+) the resultant constants are (in GHz) A = 194.3, B = 34.28, and C = 29.14; for H3COH2(+) A = 103.7, B = 21.18, and C = 20.30.
Titan's organic haze is potentially an important sink of photochemically produced carbon and nitrogen compounds. An assortment of microphysical haze models all suggest that the haze production rate is 10(exp -14) gm per square centimeter per second, within a factor of two. Spectral analysis of laboratory tholins compared to Titan's geometric albedo spectrum suggest that the laboratory material is a good analog to Titan's haze. The laboratory material has an elemental composition given approximately by C4H4N, with an uncertainty in the C/N ratio of a factor of two. Thus, the haze represents a sink for C of 4 x 10(exp 8) per square centimeter per second, and a sink for N of 1 x 10(exp 8) per square centimeter per second. The C sink is small compared to condensation of hydrocarbons but the sink for N is comparable to the total production rate of HCN. Because estimates of the eddy diffusion profile on Titan have been based on the HCN profile, inclusion of this additional sink for N will affect estimates for all transport processes in Titan's atmosphere.
This Confirmatory Sampling (CS) Report (CSR) was prepared for the National Aeronautics and Space Administration (NASA), Kennedy Space Center (KSC), Florida in accordance with the CS Work Plan (CSWP) (HGL, 2022a). The CSR summarizes investigation activities performed on the Railroad Tie Disposal Area (RTDA) of the Un-Numbered Operational Areas (UNOA), Potential Release Location (PRL) 229 located at KSC. HydroGeoLogic, Inc. (HGL) prepared this report under Contract Number 80KSC019D0012. The purpose of the CSR is to provide information to evaluate the level of potential contamination in surface water and sediment samples collected from the RTDA PRL 229 Pond and determine if contaminants pose unacceptable risks to ecological receptors. The CSR provides analytical data of 10 sets of surface water and sediment samples collected in 2021. Surface water samples were analyzed for volatile organic compounds (VOCs), semi-volatile organic compounds (SVOCs), polynuclear aromatic hydrocarbon (PAHs), metals, hardness, and salinity, while sediment samples were analyzed for metals, total organic carbon (TOC), SVOCs, PAHs, and total petroleum hydrocarbons (TPH). The CSR includes a Screening Level Ecological Risk Assessment (SLERA) conducted under KSC’s Resource Conservation and Recovery Act (RCRA) Corrective Action Program to determine if unacceptable risks to ecological receptors exist at PRL 229 Pond. Metals and PAHs were detected in surface water samples, including detections of antimony, arsenic, barium, calcium, copper, lead, magnesium, thallium, zinc, anthracene, benzo(a)anthracene, benzo(a)pyrene, benzo(b)fluoranthene, benzo(g,h,i)perylene, benzo(k)fluoranthene, chrysene, dibenzo(a,h)anthracene, fluoranthene, indeno(1,2,3-c,d)pyrene and pyrene. Based on measured salinity (1.03 to 1.21 parts per thousand), the pond was identified as freshwater. Sediment analytical results also contained detections of metals and PAHs, including antimony, arsenic, barium, beryllium, cadmium, chromium, copper, lead, mercury, nickel, selenium, thallium, zinc, anthracene benzo(a)anthracene, benzo(a)pyrene, benzo(b)fluoranthene, benzo(g,h,i)perylene, benzo(k)fluoranthene, chrysene, dibenzo(a,h)anthracene, fluoranthene, indeno(1,2,3-c,d)pyrene and pyrene. Potential risks to ecological receptors were evaluated in accordance with the SLERA Work Plan and the Decision Process Document for the Resource Conservation and Recovery Act (RCRA) Corrective Action Program at KSC (DPD) (Geosyntec, 2019), and using the 2021 sediment and surface water analytical results. Potential ecological receptors evaluated in the SLERA include the aquatic community, benthic invertebrates, aquatic birds, and aquatic mammals. The SLERA included Steps 2a through 2c of the Ecological Risk Assessment (ERA) process in accordance with the DPD. Using multiple lines of evidence, the SLERA concluded that many of the detected analytes are present at concentrations similar to those in background or reference locations at KSC, and/or at concentrations that would not pose a significant risk to ecological communities. There are no screening values available for the avian community, and PAHs are bioaccumulative compounds. For this reason, food web modeling was completed as a supplement to Step 2c to provide a quantitative evaluation of this exposure pathway. Food web modeling indicates that PAHs are unlikely to pose a risk to birds exposed to site sediment and surface water. Based on the results of the SLERA, the contaminants in sediment and surface water pose no to minimal risk to the ecological communities that could live or forage at the site. Based on the results of the SLERA, no further study is recommended for the RTDA Pond at PRL 229.
A major challenge in assessing the impact of aerosols on climate change is to understand how human activities change aerosol loading and properties relative to the pristine-preindustrial baseline. Here we combine chemical transport simulation and field measurements to investigate the effect of anthropogenic pollution from an isolated metropolis on particle number concentration over the preindustrial-like Amazon rainforest through various new-particle formation (NPF) mechanisms and primary particle emissions. To represent organic-mediated NPF, we employ a state-of-the-art model that systematically simulates the formation chemistry and thermodynamics of extremely low volatility organic compounds as well as their roles in NPF processes, and further update the model to improve organic NPF simulations under human-influenced conditions. Results show that urban pollution from the metropolis increases particle number concentration by a factor of 5–25 over the downwind region (within 200 km from city center) compared to background conditions. Overall, our model indicates that NPF contributes over 70% of the total particle number in the downwind region except immediately adjacent to the sources. Among different NPF mechanisms, the ternary NPF involving organics and sulfuric acid overwhelmingly dominates. The improved understanding of particle formation mechanisms will help better quantify anthropogenic aerosol forcing from pristine-preindustrial times to present day.
It is well known that many chlorine and bromine compounds that are inert in the troposphere are destroyed in the stratosphere and contribute to the stratospheric burden of reactive chlorine and bromine species. But the contribution from those chlorine and bromine compounds which are reactive in the troposphere is less certain because it is not known whether convection can transport these gases to the upper troposphere rapidly enough to overcome their short tropospheric lifetimes. We examine this issue using a three-dimensional chemistry and transport model to simulate the evolution of three gases which have surface sources, bromoform (CHBr3), methyl chloroform (CH3CCl3), and carbon dioxide (CO2). Our objective is to determine if CHBr3 might enhance the lower stratospheric burden of reactive bromine. The other two gases provide tests of the quality of the simulation. Both CHBr3 and CH3CCl3 are destroyed in the troposphere by reaction with hydroxyl (OH), whose latitudinal and monthly variation is provided by a two-dimensional model and upon which a diurnal variation is imposed. Comparison of the lifetime of CH3CCl3 computed from observations (5 years) with the lifetime computed from the simulation provides an integrated test of the model's transport and photochemistry. Observations also show that CO2 exhibits a strong seasonal cycle in the northern hemisphere troposphere that is not propagated directly across the tropopause into the lower stratosphere. Thus, maintenance of the observed troposphere-stratosphere distinctness of CO2 in the presence of convection is a critical benchmark for meeting our objective.
Reliable super-resolution methods are crucial for applications like remote sensing, grid resilience and disaster impact analysis, and standoff biometrics. These methods infuse additional high-frequency information into reconstructions, allowing for better contextualization and image intelligence. However, super-resolution models can also introduce hallucinations or other unseen vulnerabilities that could be exploited by an adversary. This is further compounded by the prominence of deep learning in these models, as models are often blindly applied on out-of-distribution images. In this work, we implement adversarial attacks in common open-source super-resolution models and examine their impact on reconstructions and downstream classification tasks. We find that an adversarially trained super-resolution model can produce high-quality reconstructions that degrade downstream classifications. Moreover, these attacks do not require access to low-resolution imagery or class labels at inference time. These results demonstrate the vulnerability of super-resolution methods to malicious actors and motivates the development of a detector for super-resolution adversarial attacks. Further exploration of adversarial attacks in this domain is required to ensure trustworthiness and robustness of super-resolution models for national security applications.
Power electronics packages typically comprise a dielectric substrate bonded to a metal layer and attached to heat sinks using a low-thermal-conductivity solder. These multiple layers increase the effective thermal resistance of the package and are responsible for package failures under cyclic thermal loads. Bonding the aluminum nitride dielectric layer (AlN) directly to a low coefficient of thermal expansion (CTE) aluminum silicon carbide (AlSiC) cold plate using copper-aluminum (Cu-Al) transient liquid phase (TLP) bonding has been shown to improve the mechanical reliability of the power electronic packages while making the package more compact by bringing the cooling solutions closer to the devices. This study aims to characterize the Cu-Al bonds formed during the TLP bonding of AlSiC with three different power electronics substrates: pure aluminum nitride (AlN), aluminum (DBA), and copper (DBC). The material compositions and microstructures of the bonds were analyzed using scanning electron microscopy (SEM), x-ray spectroscopy (EDS), confocal scanning acoustic microscopy (C-SAM), and x-ray diffraction (XRD). ..alpha..-Al solid compound was identified as the dominant phase in AlN-AlSiC and Al-AlSiC, with a notable presence of SiC particles. In contrast, three intermetallic phases - ..theta..-CuAl2, ..eta..-CuAl, and ..gamma..'- Cu9Al4 - were observed in the Cu-AlSiC bond. A computational solid-state diffusion model was developed to predict the intermetallic compounds (IMCs) formed during Cu-Al TLP bonding in each system, which supported the observation that an initial Cu volume fraction of 20 % in the material layers produced a final bond composition of >95 % Al with minimal IMC growth. However, increased Cu concentrations produced higher concentration gradients, leading to increased growth of IMCs, Kirkendall voids, and interstitial cracks.
The conversion efficiency of CdTe solar cells may be improved by bandgap engineering, i.e., changing the bandgap value through the addition of Se in the absorber. The Se alloying enables a short-circuit current density improvement, as it leads to a bandgap energy value decrease. Furthermore, it has been associated with increased minority carrier lifetimes, assuring high open-circuit voltage values. An Se gradient profile control can further optimize the solar cell performance. Thus, an optical model baseline of the CdSe x Te 1–x (CST) compound was developed. Spectroscopic ellipsometry measurements were conducted to accurately extract the optical constants of ten CST layers deposited through coevaporation with x varying from 0 to 1. Using the measured dielectric function spectra from the discrete CST layers with varying x, and considering the composition-induced shift in the critical point energies, an energy-shift model was employed to develop the accurate optical library for the CST compound for any x value to provide data for future modeling and optimization. Furthermore, the library accuracy was validated through optical simulations of the quantum efficiency of a graded CST solar cell using the finite-difference time-domain method by replicating the Se profile in the absorber layer measured through secondary ion mass spectrometry.
Hydrides based on magnesium and intermetallic compounds provide a viable solution to the challenge of energy storage from renewable sources, thanks to their ability to absorb and desorb hydrogen in a reversible way with a proper tuning of pressure and temperature conditions. Therefore, they are expected to play an important role in the clean energy transition and in the deployment of hydrogen as an efficient energy vector. This review, by experts of Task 40 ‘Energy Storage and Conversion based on Hydrogen’ of the Hydrogen Technology Collaboration Programme of the International Energy Agency, reports on the latest activities of the working group ‘Magnesium- and Intermetallic alloys-based Hydrides for Energy Storage’. The following topics are covered by the review: multiscale modelling of hydrides and hydrogen sorption mechanisms; synthesis and processing techniques; catalysts for hydrogen sorption in Mg; Mg-based nanostructures and new compounds; hydrides based on intermetallic TiFe alloys, high entropy alloys, Laves phases, and Pd-containing alloys. Finally, an outlook is presented on current worldwide investments and future research directions for hydrogen-based energy storage.
The results are presented of the Alternative Fluorocarbon Environmental Acceptability Study (AFEAS), which was organized to evaluate the potential effects on the environment of alternate compounds targeted to replace fully halogenated chlorofluorocarbons (CFCs). All relevant current scientific information to determine the environmental acceptability of the alternative fluorocarbons. Special emphasis was placed on: the potential of the compounds to affect stratospheric ozone; their potential to affect tropospheric ozone; their potential to contribute to model calculated global warming; the atmospheric degradation mechanisms of the compounds, in order to identify their products; and the potential environmental effects of the decomposition products. The alternative compounds to be studied were hydrofluorocarbons (HFCs) with one or two carbon atoms and one or more each of fluorine and hydrogen.
We have developed a new photochemical model of Titan's atmosphere which includes all the important compounds and reactions in spherical geometry from the surface to 1240 km. Compared to the previous model of Yung et al. (1984), the most significant recent change in the reactions used is the updated methane dissociation scheme (Mordaunt et al. 1993). Moreover, the transfer of the solar radiation in the atmosphere and the photolysis rates have been calculated by using a Monte Carlo code. Finally, the eddy diffusion coefficient profile is adjusted in order to fit the mean vertical distribution of HCN retrieved from millimeter groundbased observations of Tanguy et al. (1990); using new values for the boundary flux of atomic nitrogen (Strobel et al. 1992). We have run the model in both steady-state and diurnal modes, with 62 species involved in 249 reactions. There is little difference between diurnal and steady-state results. Overall our results are in a closer agreement with the abundances inferred from the Voyager infrared measurements at the equator than the Yung et al. results. We find that the catalytic scheme for H recombination invoked by Yung et al. only slightly improves the model results and we conclude that this scheme is not essential to fit observations.
Efficient methods for searching the chemical space of molecular compounds are needed to automate and accelerate the design of new functional molecules such as pharmaceuticals. Given the high cost in both resources and time for experimental efforts, computational approaches play a key role in guiding the selection of promising molecules for further investigation. Here, we construct a workflow to accelerate design by combining approximate quantum chemical methods [i.e. density-functional tight-binding (DFTB)], a graph convolutional neural network (GCNN) surrogate model for chemical property prediction, and a masked language model (MLM) for molecule generation. Property data from the DFTB calculations are used to train the surrogate model; the surrogate model is used to score candidates generated by the MLM. The surrogate reduces computation time by orders of magnitude compared to the DFTB calculations, enabling an increased search of chemical space. Furthermore, the MLM generates a diverse set of chemical modifications based on pre-training from a large compound library. We utilize the workflow to search for near-infrared photoactive molecules by minimizing the predicted HOMO-LUMO gap as the target property. Our results show that the workflow can generate optimized molecules outside of the original training set, which suggests that iterations of the workflow could be useful for searching vast chemical spaces in a wide range of design problems.
The adsorption energies for hydrogen, oxygen, and hydroxyl were calculated by means of density functional theory on the lowest energy surface of 24 pure metals and 332 binary intermetallic compounds with stoichiometries AB, A 2 B, and A 3 B taking into account the effect of biaxial elastic strains. This information was used to train two random forest regression models, one for the hydrogen adsorption and another for the oxygen and hydroxyl adsorption, based on 9 descriptors that characterized the geometrical and chemical features of the adsorption site as well as the applied strain. All the descriptors for each compound in the models could be obtained from physico-chemical databases. The random forest models were used to predict the adsorption energy for hydrogen, oxygen, and hydroxyl of ≈2700 binary intermetallic compounds with stoichiometries AB, A 2 B, and A 3 B made of metallic elements, excluding those that were environmentally hazardous, radioactive, or toxic. This information was used to search for potential good catalysts for the HER and ORR from the criteria that their adsorption energy for H and O/OH, respectively, should be close to that of Pt. Further, this investigation shows that the suitably trained machine learning models can predict adsorption energies with an accuracy not far away from density functional theory calculations with minimum computational cost from descriptors that are readily available in physico-chemical databases for any compound. Moreover, the strategy presented in this paper can be easily extended to other compounds and catalytic reactions, and is expected to foster the use of ML methods in catalysis.