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Smart metal-metal oxide heterointerface construction holds promising potentials to endow an efficient electron redistribution for electrochemical CO2 reduction reaction (CO2RR). However, inhibited by the intrinsic linear-scaling relationship, the binding energies of competitive intermediates will simultaneously change due to the shifts of electronic energy level, making it difficult to exclusively tailor the binding energies to target intermediates and the final CO2RR performance. Nonetheless, creating specific adsorption sites selective for target intermediates probably breaks the linear-scaling relationship. To verify it, Ag nanoclusters were anchored onto oxygen vacancy-rich CeO2 nanorods (Ag/OV-CeO2) for CO2RR, and it was found that the oxygen vacancy-driven heterointerface could effectively promote CO2RR to CO across the entire potential window, where a maximum CO Faraday efficiency (FE) of 96.3% at -0.9 V and an impressively high CO FE of over 62.3% were achieved at a low overpotential of 390 mV within a flow cell. The experimental and computational results collectively suggested that the oxygen vacancy-driven heterointerfacial charge spillover conferred an optimal electronic structure of Ag and introduced additional adsorption sites exclusively recognizable for *COOH, which, beyond the linear-scaling relationship, enhanced the binding energy to *COOH without hindering *CO desorption, thus resulting in the efficient CO2RR to CO.

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Climate change has significantly altered the characteristics of climate zones, posing considerable challenges to ecosystems and biodiversity, particularly in Borneo, known for its high species density per unit area. This study aimed to classify the region into homogeneous climate groups based on long-term average behavior. The most effective parameters from the high-resolution daily gridded Princeton climate datasets spanning 65 years (1950-2014) were utilized, including rainfall, relative humidity (RH), temperatures (Tavg, Tmin, Tmax, and diurnal temperature range (DTR)), along with elevation data at 0.25° resolution. The FCM clustering method outperformed K-Mean and two Ward's hierarchical methods (WardD and WardD2) in classifying Borneo's climate zones based on multi-criteria assessment, exhibiting the lowest average distance (2.172-2.180) and the highest compromise programming index (CPI)-based correlation ranking among cluster averages across all climate parameters. Borneo's climate zones were categorized into four: 'Wet and cold' (WC) and 'Wet' (W) representing wetter zones, and 'Wet and hot' (WH) and 'Dry and hot' (DH) representing hotter zones, each with clearly defined boundaries. For future projection, EC-Earth3-Veg ranked first for all climate parameters across 961 grid points, emerging as the top-performing model. The linear scaling (LS) bias-corrected EC-Earth3-Veg model, as shown in the Taylor diagram, closely replicated the observed datasets, facilitating future climate zone reclassification. Improved performance across parameters was evident based on MAE (35.8-94.6%), MSE (57.0-99.5%), NRMSE (42.7-92.1%), PBIAS (100-108%), MD (23.0-85.3%), KGE (21.1-78.1%), and VE (5.1-9.1%), with closer replication of empirical probability distribution function (PDF) curves during the validation period. In the future, Borneo's climate zones will shift notably, with WC elongating southward along the mountainous spine, W forming an enclave over the north-central mountains, WH shifting northward and shrinking inland, and DH expanding northward along the western coast. Under SSP5-8.5, WC is expected to expand by 39% and 11% for the mid- and far-future periods, respectively, while W is set to shrink by 46%. WH is projected to expand by 2% and 8% for the mid- and far-future periods, respectively. Conversely, DH is expected to expand by 43% for the far-future period but shrink by 42% for the mid-future period. This study fills a gap by redefining Borneo's climate zones based on an increased number of effective parameters and projecting future shifts, utilizing advanced clustering methods (FCM) under CMIP6 scenarios. Importantly, it contributes by ranking GCMs using RIMs and CPI across multiple climate parameters, addressing a previous gap in GCM assessment. The study's findings can facilitate cross-border collaboration by providing a shared understanding of climate dynamics and informing joint environmental management and disaster response efforts.

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Electrocatalytic nitrogen reduction reaction (NRR) is one of the most promising approaches to achieving green and efficient NH3 production. However, the designs of efficient NRR catalysts with high activity and selectivity still are severely hampered by inherent linear scaling relations among the adsorption energies of NRR intermediates. Herein, the properties of ten M3B4 type MBenes have been initially investigated for efficient N2 activation and reduction to NH3via first-principles calculations. We highlight that Cr3B4 MBene possesses remarkable NRR activity with a record-low limiting potential (-0.13 V). Then, this work proposes descriptor-based design principles that can effectively evaluate the catalytic activity of MBenes, which have been further employed to design bimetallic M2M'B4 MBenes. As a result, 5 promising candidates including Ti2YB4, V2YB4, V2MoB4, Nb2YB4, and Nb2CrB4 with excellent NRR performance have been extracted from 20 bimetallic MBenes. Further analysis illuminates that constructing bimetallic MBenes can selectively tune the adsorption strength of NHNH2** and NH2NH2**, and break the linear scaling relations between their adsorption energies, rendering them ideal for NRR. This work not only pioneers the application of MBenes as efficient NRR catalysts but also proposes rational design principles for boosting their catalytic performance.

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Crystal phase engineering has emerged as a powerful tool for tailoring the electrocatalytic performance, yet its impact on nitrate reduction to ammonia (NRA) remains largely uncharted territory. Herein, density functional theory (DFT) calculations are performed to unravel the influence of the crystal phase of FeOOH on the adsorption behavior of *NO3. Inspiringly, FeOOH samples with four distinct crystal phases (δ, γ, α, and ß) are successfully synthesized and deployed as electrocatalysts for NRA. Remarkably, among all FeOOH samples, δ-FeOOH demonstrates the superior NRA performance, achieving a NH3 Faradic efficiency ( FE NH 3 $\rm{FE} _ {\rm{NH_3}}$ ) of 90.2% at -1.0 V versus reversible hydrogen electrode (RHE) and a NH3 yield rate ( Yield NH 3 $\rm{Yield} _ {\rm{NH_3}}$ ) of 5.73 mg h-1 cm-2 at -1.2 V. In-depth experiments and theoretical calculations unveil the existence of hydrogen bonding interaction between δ-FeOOH and *NOx, which not only enhances the adsorption of *NOx but also disrupts the linear relationships between the free energy of *NO3 adsorption and various parameters, including limiting potential, d-band center (εd) and transferred charge from FeOOH to *NO3, ultimately contributing to the exceptional NRA performance.

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We propose an efficient approach for simultaneous prediction of thermal and electronic transport properties in complex materials. Firstly, a highly efficient machine-learned neuroevolution potential (NEP) is trained using reference data from quantum-mechanical density-functional theory calculations. This trained potential is then applied in large-scale molecular dynamics simulations, enabling the generation of realistic structures and accurate characterization of thermal transport properties. In addition, molecular dynamics simulations of atoms and linear-scaling quantum transport calculations of electrons are coupled to account for the electron-phonon scattering and other disorders that affect the charge carriers governing the electronic transport properties. We demonstrate the usefulness of this unified approach by studying electronic transport in pristine graphene and thermoelectric transport properties of a graphene antidot lattice, with a general-purpose NEP developed for carbon systems based on an extensive dataset.

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Bias correction (BC) of General Circulation Models (GCMs) variables is a common practice when it is being used for climate impact assessment studies at regional scales. The present study proposes a bias correction method (LR-Reg) that first adjusts the original GCM precipitation for local lapse rate corrections and later bias corrects the lapse rate-adjusted GCMs precipitation data with linear regression coefficients. We evaluated LR-Reg BC method in comparison to Linear Scaling (LS) and Quantile Mapping (QMap) BC methods, and NASA's downscaled NEX data for Monsoon Asia region. This study used Coupled Model Intercomparison Project Phase 6 (CMIP6)-based MIROC6 GCM precipitation with historical and projected shared socio-economic pathways (SSP) scenarios (SSP245 and SSP585) datasets. The BC comparison results show that the relative percentage reduction in mean absolute error (MAE) values of LR-Reg over LS-BC was up to 10-30% while this relative reduction in MAE values of LR-Reg was 30-50% over QMap-BC and 75-100% over NASA's NEX-data. The future projected precipitation over Monsoon Asia during dry season shows more decreased precipitation by up to 100% mostly in the south Asia while during wet season shows more increased precipitation by up to 50% mostly in the northeastern China and in the Himalayan belts with respect to the baseline condition (1970-2005). The results on the average precipitation per 0.25 degree increase in latitude analysis shows that the maximums of average monsoon precipitation during baseline period occur at 0 and 25 degree latitudes while the projected monsoon precipitation during both SSP scenarios occurs at 10 and 20 degree latitudes which clearly shows an inward shift in the latitude axis for the projected precipitation in the Monsoon Asia.

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Electrochemical carbon dioxide reduction reaction (CO2RR) involves a variety of intermediates with highly correlated reaction and ad-desorption energies, hindering optimization of the catalytic activity. For example, increasing the binding of the *COOH to the active site will generally increase the *CO desorption energy. Breaking this relationship may be expected to dramatically improve the intrinsic activity of CO2RR, but remains an unsolved challenge. Herein, we addressed this conundrum by constructing a unique atomic dispersed hetero-pair consisting of Mo-Fe di-atoms anchored on N-doped carbon carrier. This system shows an unprecedented CO2RR intrinsic activity with TOF of 3336 h-1, high selectivity toward CO production, Faradaic efficiency of 95.96% at - 0.60 V and excellent stability. Theoretical calculations show that the Mo-Fe diatomic sites increased the *COOH intermediate adsorption energy by bridging adsorption of *COOH intermediates. At the same time, d-d orbital coupling in the Mo-Fe di-atom results in electron delocalization and facilitates desorption of *CO intermediates. Thus, the undesirable correlation between these steps is broken. This work provides a promising approach, specifically the use of di-atoms, for breaking unfavorable relationships based on understanding of the catalytic mechanisms at the atomic scale.

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The NAO2GTO scheme provides an efficient way to evaluate the electron repulsion integrals (ERIs) over numerical atomic orbitals (NAOs) with auxiliary Gaussian-type orbitals (GTOs). However, the NAO2GTO fitting will significantly impact the accuracy and convergence of hybrid functional calculations. To address this issue, here we propose to use the fitted orbitals as a new numerical basis to properly handle the mismatch between NAOs and fitted GTOs. We present an efficient and linear-scaling implementation of analytical gradients of Hartree-Fock exchange (HFX) energy for periodic HSE06 calculations with fitted NAOs in the HONPAS package. In our implementation, the ERIs and their derivatives for HFX matrix and forces are evaluated analytically with the auxiliary GTOs, while other terms are calculated using numerically discretized GTOs. Several integral screening techniques are employed to reduce the number of required ERI derivatives. We benchmark the accuracy and efficiency of our implementation and demonstrate that our results of lattice constants, bulk moduli, and band gaps of several typical semiconductors are in good agreement with the experimental values. We also show that the calculation of HFX forces based on a master-worker dynamic parallel scheme has a very high efficiency and scales linearly with respect to system size. Finally, we study the geometry optimization and polaron formation due to an excess electron in rutile TiO2 by means of HSE06 calculations to further validate the applicability of our implementation.

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We present a code modularization approach to design efficient and massively parallel cubic- and linear-scaling solvers for electronic structure calculations using atomic orbitals. The modular implementation of the orbital minimization method, in which linear algebra and parallelization issues are handled via external libraries, is demonstrated in the SIESTA code. The distributed block compressed sparse row (DBCSR) and scalable linear algebra package (ScaLAPACK) libraries are used for algebraic operations with sparse and dense matrices, respectively. The MatrixSwitch and libOMM libraries, recently developed within the Electronic Structure Library, facilitate switching between different matrix formats and implement the energy minimization. We show results comparing the performance of several cubic-scaling algorithms, and also demonstrate the parallel performance of the linear-scaling solvers, and their supremacy over the cubic-scaling solvers for insulating systems with sizes of several hundreds of atoms.

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The objective of this study is to investigate and perform long-term forecasting of both streamflow and hydrological drought over Ethiopia. Observed streamflow and precipitation data are collected from 17 streamflow stations and 34 rainfall gauge stations to forecast future streamflow and hydrological drought from 2026 to 2099. Streamflow forecasting is performed using an artificial neural network (ANN) in conjunction with python software. Observed precipitation and streamflow data from 1973 to 2014 are used to train and test the ANN model by 70 and 30% ratios, respectively. After training the model, future downscaled precipitation data from regional climate models (RCM) have been used as input data to forecast future streamflow. Three RCM models were used to downscale historical and future climate data. RACMO is found a good downscaling model for all selected stations. The linear scaling bias correction technique results in less than 2% error compared to other alternative techniques. The result indicates that ANN is a good tool to forecast streamflow in areas having a good correlation between precipitation and streamflow such as Abbay, Awash, Baro, Omo Gibe, and Tekeze river basins. But in arid areas for example Genale Dawa, Wabishebele, and Rift Valley basins, the model is not suitable because the input data (precipitation) have high variation than the output variable (streamflow). In such areas, meteorological drought analysis and forecasting are better than hydrological drought analysis. Finally, future hydrological drought is analyzed using forecasted streamflow data as input to the streamflow drought index (SDI). The result indicates that 2028, 2036, 2042, 2044, 2062, and 2063 are the expected extreme drought years in most river basins of Ethiopia in the future. This shows that at least one extreme drought is expected in each decade in the future. Therefore, extensive research in drought analysis and forecasting is needed to develop an effective drought early warning system, and water resource management policy.

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Abundant availability of seawater grants economic and resource-rich benefits to water electrolysis technology requiring high-purity water if undesired reactions such as chlorine evolution reaction (CER) competitive to oxygen evolution reaction (OER) are suppressed. Inspired by a conceptual computational work suggesting that OER is kinetically improved via a double activation within 7 Å-gap nanochannels, RuO2 catalysts are realized to have nanoscopic channels at 7, 11, and 14 Å gap in average (dgap ), and preferential activity improvement of OER over CER in seawater by using nanochanneled RuO2 is demonstrated. When the channels are developed to have 7 Å gap, the OER current is maximized with the overpotential required for triggering OER minimized. The gap value guaranteeing the highest OER activity is identical to the value expected from the computational work. The improved OER activity significantly increases the selectivity of OER over CER in seawater since the double activation by the 7 Å-nanoconfined environments to allow an OER intermediate (*OOH) to be doubly anchored to Ru and O active sites does not work on the CER intermediate (*Cl). Successful operation of direct seawater electrolysis with improved hydrogen production is demonstrated by employing the 7 Å-nanochanneled RuO2 as the OER electrocatalyst.

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Lithium-manganese-oxide (Li-Mn-O) spinel is among the promising and economically viable, high-energy density cathode materials for enhancing the performance of lithium-ion batteries. However, its commercialization is hindered by its poor cyclic performance. In computational modelling, pivotal in-depth understanding of material behaviour and properties is sizably propelled by advancements in computational methods. Hence, the current work compares traditional DFT (CASTEP) and linear-scaling DFT (ONETEP) in a LiMn2O4 electronic property study to pave way for large-scale DFT calculations in a quest to improve its electrochemical properties. The metallic behaviour of LixMn2O4 (0.25 ≤ x ≤ 1) and Li2Mn2O4 was correctly determined by both CASTEP and ONETEP code in line with experiments. Furthermore, OCV during the discharge cycle deduced by both codes is in good accordance and is between 5 V and 2.5 V in the composition range of 0 ≤ x ≤ 1. Moreover, the scaling of the ONETEP code was performed at South Africa's CHPC to provide guidelines on more productive large-scale ONETEP runs. Substantial total computing time can be saved by systematically adding the number of processors with the growing structure size. The study also substantiates that true linear scaling of the ONETEP code is achieved by a systematic truncation of the density kernel.

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Determination of the hip joint centre (HJC) is important to accurately estimate hip joint motion, moments and muscle forces. The most accurate method for HJC estimation without medical imaging is an area of interest in the biomechanics community, especially in a paediatric population, which has not been widely evaluated. HJC locations were calculated by sphere-fitting to the acetabulum of three-dimensional pelvises segmented from 333 CT scans of children aged 4 to 18 years old. Three methods for determining the HJC were compared: regression equations, linear scaling, and shape model prediction. The new regression equations developed in this study produced Euclidean distance errors of 6.23 mm ± 2.90 mm. Linear scaling of paediatric bone produced errors of 3.90 mm ± 2.52 mm and adult bone scaling of 5.45 mm ± 3.26 mm. Prediction of the HJC using a paediatric statistical shape model produced mean Euclidian distance errors of 2.95 mm ± 1.65 mm. Overall, shape model prediction of the HJC produced the lowest errors, with linear scaling of a mean paediatric pelvis providing better estimates than regression equations.

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Developing efficient oxygen reduction reaction (ORR) electrocatalysts is critical to fuel cells and metal-oxygen batteries, but also greatly hindered by the limited Pt resources and the long-standing linear scaling relationship (LSR). In this study, ∼6 nm and highly uniform Pd nanospheres (NSs) having surface-doped (SD) P-O species are synthesized and evenly anchored onto carbon blacks, which are further simply heat-treated (HT). Under alkaline conditions, Pd/SDP-O NSs/C-HT exhibits respective 8.7 (4.3)- and 5.0 (5.5)-fold enhancements in noble-metal-mass- and area-specific activity (NM-MSA and ASA) compared with the commercial Pd/C (Pt/C). It also possesses an improved electrochemical stability. Besides, its acidic ASA and NM-MSA are 2.9 and 5.1 times those of the commercial Pd/C, respectively, and reach 65.4 and 51.5% of those of the commercial Pt/C. Moreover, it also shows nearly ideal 4-electron ORR pathways under both alkaline and acidic conditions. The detailed experimental and theoretical analyses reveal the following: (1) The electronic effect induced by the P-O species can downshift the surface d-band center to weaken the intermediate adsorptions, thus preserving more surface active sites. (2) More importantly, the potential hydrogen bond between the O atom in the P-O species and the H atom in the hydrogen-containing intermediates can in turn stabilize their adsorptions, thus breaking the ORR LSR toward more efficient ORRs and 4-electron pathways. This study develops a low-cost and high-performance ORR electrocatalyst and proposes a promising strategy for breaking the ORR LSR, which may be further applied in other electrocatalysis.

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Recent advances in alkaline earth (Ae) metal hydrogenation catalysis have broadened the spectrum of potential catalysts to include candidates from the main group, providing a sustainable alternative to the commonly used transition metals. Although Ae-amides have already been demonstrated to catalyze hydrogenation of imines and alkenes, a lucid understanding of how different metal/ligand combinations influence the catalytic activity is yet to be established. In this article, we use linear scaling relationships and molecular volcano plots to assess the potential of the Ae metal-based catalysts for the hydrogenation of alkenes. By analyzing combinations of eight metals (mono-, bi-, tri-, and tetravalent) and seven ligands, we delineate the impact of metal-ligand interplay on the hydrogenation activity. Our findings highlight that the catalytic activity is majorly determined by the charge and the size of the metal ions. While bivalent Ae metal cations delicately regulate the binding and the release of the reactants and the products, respectively, providing the right balance for this reaction, ligands play only a minor role in determining their catalytic activity. We show how volcano plots can be utilized for the rapid screening of prospective Ae catalysts to establish a guideline to achieve maximum activity in facilitating the hydrogenation process. SUPPLEMENTARY INFORMATION: The online version of this article at 10.1007/s11244-021-01480-7.

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We review a suite of stochastic vector computational approaches for studying the electronic structure of extended condensed matter systems. These techniques help reduce algorithmic complexity, facilitate efficient parallelization, simplify computational tasks, accelerate calculations, and diminish memory requirements. While their scope is vast, we limit our study to ground-state and finite temperature density functional theory (DFT) and second-order many-body perturbation theory. More advanced topics, such as quasiparticle (charge) and optical (neutral) excitations and higher-order processes, are covered elsewhere. We start by explaining how to use stochastic vectors in computations, characterizing the associated statistical errors. Next, we show how to estimate the electron density in DFT and discuss effective techniques to reduce statistical errors. Finally, we review the use of stochastic vectors for calculating correlation energies within the second-order Møller-Plesset perturbation theory and its finite temperature variational form. Example calculation results are presented and used to demonstrate the efficacy of the methods.

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Se-based nanoalloys as an emerging class of metal chalcogenide with tunable crystalline structure, component distribution, and electronic structure have attracted considerable interest in renewable energy conversion and utilization. In this Letter, we report a series of nanosized M-Se catalysts (M = Cu, Ni, Co) as prepared from laser ablation method and screen their electrocatalytic performance for onsite H2O2 generation from selective oxygen reduction reaction (ORR) in alkaline media. A flexible control on 2e-/4e- ORR pathway has been achieved by engineering the alloying component. Moreover, through a feedback loop between theory and experiment an optimized scaling relationship between oxygenated ORR intermediates has been discovered on cubic Cu7.2Se4 nanocrystals, that is, the ensemble effect of isolated Cu component destabilizes O* binding while the ligand effect of Se to Cu fine-tunes the binding strength of OOH*, leading to a superb H2O2 selectivity above 90% over a wide potential window even after 1400 potential cycles.

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Humans grossly underestimate exponential growth, but are at the same time overconfident in their (poor) judgement. The so-called 'exponential growth bias' is of new relevance in the context of COVID-19, because it explains why humans have fundamental difficulties to grasp the magnitude of a spreading epidemic. Here, we addressed the question, whether logarithmic scaling and contextual framing of epidemiological data affect the anticipation of exponential growth. Our findings show that underestimations were most pronounced when growth curves were linearly scaled and framed in the context of a more advanced epidemic progression. For logarithmic scaling, estimates were much more accurate, on target for growth rates around 31%, and not affected by contextual framing. We conclude that the logarithmic depiction is conducive for detecting exponential growth during an early phase as well as resurgences of exponential growth.

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In the linear-scaling divide-and-conquer (DC) electronic structure method, each subsystem is calculated together with the neighboring buffer region, the size of which affects the energy error introduced by the fragmentation in the DC method. The DC self-consistent field calculation utilizes a scheme to automatically determine the appropriate buffer region that is as compact as possible for reducing the computational time while maintaining acceptable accuracy (J. Comput. Chem. 2018, 39, 909). To extend the automatic determination scheme of the buffer region to the DC second-order Møller-Plesset perturbation (MP2) calculation, a scheme for estimating the subsystem MP2 correlation energy contribution from each atom in the buffer region is proposed. The estimation is based on the atomic orbital Laplace MP2 formalism. Based on this, an automatic buffer determination scheme for the DC-MP2 calculation is constructed and its performance for several types of systems is assessed.

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Electrocatalytic conversion of carbon dioxide into high-value multicarbon (C2+ ) chemical feedstocks offers a promising avenue to liberate the chemical industry from fossil-resource dependence and eventually close the anthropogenic carbon cycle but is severely impeded by the lack of high-performance catalysts. To break the linear scaling relationship of intermediate binding and minimize the kinetic barrier of CO2 reduction reactions, ternary Cu-Au/Ag nanoframes were fabricated to decouple the functions of CO generation and C-C coupling, whereby the former is promoted by the alloyed Ag/Au substrate and the latter is facilitated by the highly strained and positively charged Cu domains. Thus, C2 H4 production in an H-cell and a flow cell occurred with high Faradic efficiencies of 69±5 and 77±2 %, respectively, as well as good electrocatalytic stability and material durability. In situ IR and DFT calculations unveiled two competing pathways for C2 H4 generation, of which direct CO dimerization is energetically favored.