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At least 19 records

Reversing Size-Dependent Trends in the Oxidation of Copper Clusters through Support Effects: Reversing Size-Dependent Trends in the Oxidation of Copper Clusters through Support Effects

Having the ability to tune the oxidation state of Cu nanoparticles is essential for their utility as catalysts. The degree of oxidation that maximizes product yield and selectivity is known to vary, depending on the particular reaction. Using first principles calculations and XANES measurements, we show that for subnanometer sizes in the gas phase, smaller Cu clusters are more resistant to oxidation. However, this trend is reversed upon deposition on an alumina support. We are able to explain this result in terms of strong cluster-support interactions, which differ significantly for the oxidized and elemental clusters. The stable cluster phases also feature novel oxygen stoichiometries. Our results suggest that one can tune the degree of oxidation of Cu catalysts by optimizing not just their size, but also the support they are deposited on.

ab initio calculations↗

Observationally derived rise in methane surface forcing mediated by water vapour trend

Atmospheric methane (CH4) mixing ratios exhibited a plateau between 1995 and 2006 and have subsequently been increasing. While there are a number of competing explanations for the temporal evolution of this greenhouse gas, these prominent features in the temporal trajectory of atmospheric CH4 are expected to perturb the surface energy balance through radiative forcing, largely due to the infrared radiative absorption features of CH4. However, to date this has been determined strictly through radiative transfer calculations. Here, we present a quantified observation of the time series of clear-sky radiative forcing by CH4 at the surface from 2002 to 2012 at a single site derived from spectroscopic measurements along with line-by-line calculations using ancillary data. There was no significant trend in CH4 forcing between 2002 and 2006, but since then, the trend in forcing was 0.026?±?0.006 (99.7%?CI)?W?m2?yr-1. The seasonal-cycle amplitude and secular trends in observed forcing are influenced by a corresponding seasonal cycle and trend in atmospheric CH4. However, we find that we must account for the overlapping absorption effects of atmospheric water vapour (H2O) and CH4 to explain the observations fully. Thus, the determination of CH4 radiative forcing requires accurate observations of both the spatiotemporal distribution of CH4 and the vertically resolved trends in H2O.

Feldman, Daniel R.↗

Decadal trends in the seasonal-cycle amplitude of terrestrial CO 2 exchange resulting from the ensemble of terrestrial biosphere models

The seasonal-cycle amplitude (SCA) of the atmosphere–ecosystem carbon dioxide (CO 2 ) exchange rate is a useful metric of the responsiveness of the terrestrial biosphere to environmental variations. It is unclear, however, what underlying mechanisms are responsible for the observed increasing trend of SCA in atmospheric CO 2 concentration. Using output data from the Multi-scale Terrestrial Model Intercomparison Project (MsTMIP), we investigated how well the SCA of atmosphere–ecosystem CO 2 exchange was simulated with 15 contemporary terrestrial ecosystem models during the period 1901–2010. Also, we made attempt to evaluate the contributions of potential mechanisms such as atmospheric CO 2 , climate, land-use, and nitrogen deposition, through factorial experiments using different combinations of forcing data. Under contemporary conditions, the simulated global-scale SCA of the cumulative net ecosystem carbon flux of most models was comparable in magnitude with the SCA of atmospheric CO 2 concentrations. Results from factorial simulation experiments showed that elevated atmospheric CO 2 exerted a strong influence on the seasonality amplification. When the model considered not only climate change but also land-use and atmospheric CO 2 changes, the majority of the models showed amplification trends of the SCAs of photosynthesis, respiration, and net ecosystem production (+0.19 % to +0.50 % yr -1 ). In the case of land-use change, it was difficult to separate the contribution of agricultural management to SCA because of inadequacies in both the data and models. The simulated amplification of SCA was approximately consistent with the observational evidence of the SCA in atmospheric CO 2 concentrations. Large inter-model differences remained, however, in the simulated global tendencies and spatial patterns of CO 2 exchanges. Further studies are required to identify a consistent explanation for the simulated and observed amplification trends, including their underlying mechanisms. Nevertheless, this study implied that monitoring of ecosystem seasonality would provide useful insights concerning ecosystem dynamics.

54 ENVIRONMENTAL SCIENCES↗

North American extreme precipitation events and related large-scale meteorological patterns: a review of statistical methods, dynamics, modeling, and trends

This paper surveys the current state of knowledge regarding large-scale meteorological patterns (LSMPs) associated with short-duration (less than 1 week) extreme precipitation events over North America. In contrast to teleconnections, which are typically defned based on the characteristic spatial variations of a meteorological feld or on the remote circulation response to a known forcing, LSMPs are defned relative to the occurrence of a specifc phenomenon—here, extreme precipitation—and with an emphasis on the synoptic scales that have a primary infuence in individual events, have medium-range weather predictability, and are well-resolved in both weather and climate models. For the LSMP relationship with extreme precipitation, we consider the previous literature with respect to defnitions and data, dynamical mechanisms, model representation, and climate change trends. There is considerable uncertainty in identifying extremes based on existing observational precipitation data and some limitations in analyzing the associated LSMPs in reanalysis data. Many diferent defnitions of “extreme” are in use, making it difcult to directly compare diferent studies. Dynamically, several types of meteorological systems—extratropical cyclones, tropical cyclones, mesoscale convective systems, and mesohighs—and several mechanisms—fronts, atmospheric rivers, and orographic ascent—have been shown to be important aspects of extreme precipitation LSMPs. The extreme precipitation is often realized through mesoscale processes organized, enhanced, or triggered by the LSMP. Understanding of model representation, trends, and projections for LSMPs is at an early stage, although some promising analysis techniques have been identifed and the LSMP perspective is useful for evaluating the model dynamics associated with extremes.

Barlow, Mathew↗

Global, Regional, and Megacity Trends in the Highest Temperature of the Year: Diagnostics and Evidence for Accelerating Trends

Trends in short-lived high-temperature extremes record a different dimension of change than the extensively studied annual and seasonal mean daily temperatures. They also have important socioeconomic, environmental, and human health implications. Here, we present analysis of the highest temperature of the year for approximately 9000 stations globally, focusing on quantifying spatially explicit exceedance probabilities during the recent 50- and 30-year periods. A global increase of 0.19°Cper decade during the past 50 years (through 2015) accelerated to 0.25°C per decade during the last 30 years, a faster increase than in the mean annual temperature. Strong positive 30-year trends are detected in large regions of Eurasia and Australia with rates higher than 0.60°C per decade. In cities with more than 5 million inhabitants, where most heat-related fatalities occur, the average change is 0.33°C per decade, while some east Asia cities, Paris, Moscow, and Houston have experienced changes higher than 0.60°Cper decade.

54 ENVIRONMENTAL SCIENCES↗

Trends in homolytic bond dissociation energies of five- and six-coordinate hydrides of Group 9 transition metals: Co, Rh, Ir

The homolytic bond dissociation energies of a series of five- and six-coordinate monoand di-hydride complexes of the type HM(diphosphine)2 and [H2M(diphosphine)2]+ (where M = Co, Rh and Ir) are calculated and compared with experimental values. This work probes the relationship between the homolytic bond dissociation energies (HMBDEs) of these complexes in these two different coordination environments and formal oxidation states. The results of these calculations and previous experimental observations suggest that for M = Rh the HMBDE of the five-coordinate HM(diphosphine)2 species are 0-2 kcal/mol larger than the HMBDE of the corresponding six-coordinate [H2M(diphosphine)2]+ species. For M = Ir the bond energies of the fiveand six-coordinate complexes are nearly the same and for M = Co the six-coordinate species are 1-5 kcal/mol less than the corresponding five-coordinate species. Simplified models of large and complicated ligands seem to capture the essential trends and give very good estimates of these thermodynamic properties in such systems.

homolytic bond dissociation, metal hydrides, dipho↗

Multivariate zeolitic imidazolate frameworks with an inverting trend in flexibility

Here, through systematic linker substitution in a flexible zeolitic imidazolate framework (ZIF) with step-shaped adsorption–desorption, structural intermediates between the known open and closed phases were isolated. Reflecting this, modulative sorption behaviour with an inverting adsorption pressure trend—in which the step pressure decreases and then increases again with increasing mixed linker concentration—is observed, highlighting how linker substitution modifies the energetic landscape of framework flexibility

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

New trends in photonic switching and optical networking architectures for data centers and computing systems [Invited]

The rapid increases in data traffic coupled with user preferences are driving the data center and computing system service providers to offer energy-efficient, intelligent, flexible, cost-effective, high-capacity, and low-latency data services without added complexity to the users. Disaggregated heterogeneous reconfigurable computing systems realized by photonic switching and interconnects can enhance throughput and energy efficiency for artificial intelligence/machine learning (AI/ML) workloads, especially when aided by the AI/ML-enhanced control plane. Photonic switching and new optical networking architectures are expected to solve many of these challenging problems. This paper discusses new trends in photonic switching and optical network architectures for future data centers and computing systems summarized as follows: (1) flat reconfigurable disaggregated computing enabled by high-radix photonic switching and interconnects in data centers; (2) chiplet-based computing architectures empowered by embedded photonics toward heterogeneous reconfigurable computing; (3) nanosecond-scale photonic switching in data centers and computing systems; (4) AI/ML in self-driving, application-aware, and situation-aware data centers; (5) the emergence of flexible networking for cloud computing, edge computing, and split computing, as well as flexible networking for 5G/6G RF-optical networks; and (6) the deployment of embedded co-designed silicon photonics being considered for future data centers.

Yoo, S. J. Ben (ORCID:0000000274201871)↗

Failure diagnosis and trend‐based performance losses routines for the detection and classification of incidents in large‐scale photovoltaic systems

Abstract Fault detection and classification in photovoltaic (PV) systems through real‐time monitoring is a fundamental task that ensures quality of operation and significantly improves the performance and reliability of operating systems. Different statistical and comparative approaches have already been proposed in the literature for fault detection; however, accurate classification of fault and loss incidents based on PV performance time series remains a key challenge. Failure diagnosis and trend‐based performance loss routines were developed in this work for detecting PV underperformance and accurately identifying the different fault types and loss mechanisms. The proposed routines focus mainly on the differentiation of failures (e.g., inverter faults) from irreversible (e.g., degradation) and reversible (e.g., snow and soiling) performance loss factors based on statistical analysis. The proposed routines were benchmarked using historical inverter data obtained from a 1.8 MWp PV power plant. The results demonstrated the effectiveness of the routines for detecting failures and loss mechanisms and the capability of the pipeline for distinguishing underperformance issues using anomaly detection and change‐point (CP) models. Finally, a CP model was used to extract significant changes in time series data, to detect soiling and cleaning events and to estimate both the performance loss and degradation rates of fielded PV systems.

14 SOLAR ENERGY↗

Understanding trends in electrochemical carbon dioxide reduction rates

Electrochemical carbon dioxide reduction to fuels presents one of the great challenges in chemistry. Herein we present an understanding of trends in electrocatalytic activity for carbon dioxide reduction over different metal catalysts that rationalize a number of experimental observations including the selectivity with respect to the competing hydrogen evolution reaction. We also identify two design criteria for more active catalysts. The understanding is based on density functional theory calculations of activation energies for electrochemical carbon monoxide reduction as a basis for an electrochemical kinetic model of the process. Furthermore, we develop scaling relations relating transition state energies to the carbon monoxide adsorption energy and determine the optimal value of this descriptor to be very close to that of copper.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Data Trend Shifts Induced by Method of Concentration for Trace Metals in Seawater: Automated Online Preconcentration vs Borohydride Reductive Coprecipitation of Nearshore Seawater Samples for Analysis of Ni, Cu, Zn, Cd, and Pb via ICP-MS

This research compares performance, reproducibility, and detection limits of ambient seawater analysis for trace metals using both borohydride reductive co-precipitation and an automated chelation column (seaFAST™ 2) co-preconcentration for matrix interferent elimination on total and dissolved grab samples in nearshore to marine waters, over a broad concentration range, prior to ICP-MS injection. A move to an online preconcentration method both minimizes sample preparation, and eliminates correction errors when accounting for trace impurities in precipitated samples, induced via reagents. The reproducability of the online preconcentration method described, coupled with low blanks and method detection limits (MDLs), demonstrates an effective move to an automated procedure using ethylenediaminetriacetic and iminodiacetate acid chelation exchange resin and multi-analyte determination via ICP-MS for total and dissolved Ni, Cu, Zn, Cd, and Pb in marine water samples. Average CASS-5 recoveries using the online preconcentration method (n=9) were 109±7%, 104±5%, 103±7%, 101±3%, and 86±8% respectively. The MDLs obtained from the automated method for Ni, Cu, Zn, Cd and Pb were 3.3, 1.8, 13.5, 4, and 10 times lower, respectively, than for the Borohydride method. There were statistically significant differences between the methods for CASS-5 recoveries of Ni, Cu, Zn (p < 0.0001), and Pb (p = 0.0024). Comparison of methods gave high concordance (rC = 0.90) between methods for total and dissolved Ni, Cu, Zn, and Pb, and total Cd.

seaFAST, borohydride, seawater, Ethylenediaminetet↗