Origin of Weaker Fermi Level Pinning and Localized Interface States at Metal Silicide Schottky Barriers

The Schottky barriers of transition metal silicides on silicon are characterized by two anomalous features, a face dependence of Schottky barrier heights (SBHs) and a weaker than expected dependence of SBHs on work function or “weaker Fermi level pinning.” Density functional supercell calculations reported here find that these features arise from the occurrence of localized gap states at interfacial coordination defects, in addition to the usual metal-induced gap states (MIGSs), and these lead to pinning energies that increase sequentially across the Si gap from PtSi2 to YbSi2. The interfacial gap states vary in shape with face orientation and cause the unusual face-dependent SBHs. The localized interface defect states are a key missing addition to the MIGS model, which are needed to describe fully the interface bonding such as face orientation or coordination defects. This anomalous Fermi level pinning does not reduce gap state densities but could be used to better control SBHs by creating specific configurations with near band edge pinning energies, thus giving low contact resistances in highly scaled silicon devices or 2D semiconductors

Extending the metal-induced gap state model of Schottky barriers

Fermi level pinning at Schottky barriers strongly limits the minimization of contact resistances in devices and thereby limits the scaling of modern Si electronic devices, so it is useful to understand the full range of behaviors of Schottky barriers. The authors find that some semiconductor interfaces with compound metals like silicides have apparently weaker Fermi level pinning. This occurs as these metals have an underlying covalent skeleton, whose interfaces with semiconductors lead to miscoordinated defect sites that create additional localized interface states that go beyond the standard metal-induced gap states (MIGSs) model of Schottky barriers. This causes a stronger dependence of Schottky barrier height on the metal and on interface orientation. These states are argued to be an additional component needed to extend the MIGS model

Termination-dependence of Fermi level pinning at rare-earth arsenide/GaAs interfaces

ABSTRACT The properties of metal/semiconductor interfaces are generally described by the metal-induced gap states (MIGS) model. However, rare-earth (RE) arsenide interfaces are found not to follow the MIGS model in having very different Schottky barrier heights (SBHs) for the Ga- or As-terminations of polar (100) or (111) RE-As/GaAs interfaces. Density function supercell calculations find this effect is due to localized defect interface states located on the mis-coordinated atoms of these interfaces that pin their SBHs at very different energies for each termination as determined by the anion sublattice bonding. Band offsets of semiconducting ScN/GaN interfaces also depend on their termination as determined by the same defect interface states. This pinning mechanism dominates any MIGS mechanism when it arises. Nonpolar (110) interfaces have little change in bonding, so they have no defect interface states, and we find their SBH is pinned by MIGS at the charge neutrality level. Hence, traditional MIGS models should be extended to include such interface states in a more general description

Structural modulation and assembling of metal halide perovskites for solar cells and light-emitting diodes

Funder: Singapore Economic Development BoardFunder: Energy Market Authority of Singapore; Id: http://dx.doi.org/10.13039/501100001447Funder: National Research Foundation Singapore; Id: http://dx.doi.org/10.13039/501100001381Funder: National University of Singapore; Id: http://dx.doi.org/10.13039/501100001352Funder: International Postdoctoral Exchange Fellowship Program (Talent‐Introduction Program) of ChinaFunder: Boya Postdoctoral program of Peking UniversityAbstract: Metal halide perovskites possess appealing optoelectronic properties and have been widely applied for solar energy harvesting and light emitting. Although perovskite solar cells (PeSCs) and perovskite light‐emitting diodes (PeLEDs) have been developed rapidly in recent years, there are still no universal rules for the selection of perovskites to achieve high‐performance optoelectronic devices. In this review, the working mechanisms of PeSCs and PeLEDs are first demonstrated with the discussion on the factors which determine the device performance. We then examine the optoelectronic properties of perovskites with structures modulated from 3D, 2D, 1D to 0D, and analyze the corresponding structure‐property relationships in terms of photo‐electric and electric‐photo conversion processes. Based on the unique optoelectronic properties of structurally modulated perovskites, we put forward the concept of structural assembling engineering that integrate the merits of different types of perovskites within one matrix and elaborate their excellent properties for applications of both PeSCs and PeLEDs. Finally, we discuss the potential challenges and provide our perspectives on the structural assembling engineering of perovskites for future optoelectronic applications. imag

Mapping winter wheat with combinations of temporally aggregated Sentinel-2 and Landsat-8 data in Shandong Province, China

Winter wheat is one of the major cereal crops in China. The spatial distribution of winter wheat planting areas is closely related to food security; however, mapping winter wheat with time-series finer spatial resolution satellite images across large areas is challenging. This paper explores the potential of combining temporally aggregated Landsat-8 OLI and Sentinel-2 MSI data available via the Google Earth Engine (GEE) platform for mapping winter wheat in Shandong Province, China. First, six phenological median composites of Landsat-8 OLI and Sentinel-2 MSI reflectance measures were generated by a temporal aggregation technique according to the winter wheat phenological calendar, which covered seedling, tillering, over-wintering, reviving, jointing-heading and maturing phases, respectively. Then, Random Forest (RF) classifier was used to classify multi-temporal composites but also mono-temporal winter wheat development phases and mono-sensor data. The results showed that winter wheat could be classified with an overall accuracy of 93.4% and F1 measure (the harmonic mean of producer’s and user’s accuracy) of 0.97 with temporally aggregated Landsat-8 and Sentinel-2 data were combined. As our results also revealed, it was always good to classify multi-temporal images compared to mono-temporal imagery (the overall accuracy dropped from 93.4% to as low as 76.4%). It was also good to classify Landsat-8 OLI and Sentinel-2 MSI imagery combined instead of classifying them individually. The analysis showed among the mono-temporal winter wheat development phases that the maturing phase’s and reviving phase’s data were more important than the data for other mono-temporal winter wheat development phases. In sum, this study confirmed the importance of using temporally aggregated Landsat-8 OLI and Sentinel-2 MSI data combined and identified key winter wheat development phases for accurate winter wheat classification. These results can be useful to benefit on freely available optical satellite data (Landsat-8 OLI and Sentinel-2 MSI) and prioritize key winter wheat development phases for accurate mapping winter wheat planting areas across China and elsewhere

Carbonaceous matter in the atmosphere and glaciers of the Himalayas and the Tibetan plateau: An investigative review

Carbonaceous matter, including organic carbon (OC) and black carbon (BC), is an important climate forcing agent and contributes to glacier retreat in the Himalayas and the Tibetan Plateau (HTP). The HTP – the so-called “Third Pole” – contains the most extensive glacial area outside of the polar regions. Considerable research on carbonaceous matter in the HTP has been conducted, although this research has been challenging due to the complex terrain and strong spatiotemporal heterogeneity of carbonaceous matter in the HTP. A comprehensive investigation of published atmospheric and snow data for HTP carbonaceous matter concentration, deposition and light absorption is presented, including how these factors vary with time and other parameters. Carbonaceous matter concentrations in the atmosphere and glaciers of the HTP are found to be low. Analysis of water-insoluable organic carbon and BC from snowpits reveals that concentrations of OC and BC in the atmosphere and glacier samples in arid regions of the HTP may be overestimated due to contributions from inorganic carbon in mineral dust. Due to the remote nature of the HTP, carbonaceous matter found in the HTP has generally been transported from outside the HTP (e.g., South Asia), although local HTP emissions may also be important at some sites. This review provides essential data and a synthesis of current thinking for studies on atmospheric transport modeling and radiative forcing of carbonaceous matter in the HTP

Efficacy of Minocycline in Acute Ischemic Stroke: A Systematic Review and Meta-Analysis of Rodent and Clinical Studies

Objectives: This study aimed to assess the efficacy of minocycline for the treatment of acute ischemic stroke.Background: While there have been meta-analysis that surveyed the efficacy of minocycline in the treatment of acute stroke, they have some methodological limitations. We performed a new systematic review which was distinct from previous one by adding new outcomes and including new studies.Methods: Document retrieval was executed through PubMed, Cochrane Central Register of Controlled Trials, the Stroke Center, NIH's Clinical Trials, Current Controlled Trials, and the WHO International Clinical Trials Registry Platform Search Portal before Jan 2018. The data meeting the inclusion criteria were extracted. Before meta-analysis, publication bias and heterogeneity of included studies were surveyed. Random and fixed-effects models were employed to calculate pooled estimates and 95% confidence intervals (CIs). Additionally, sensitivity and subgroup analyses were implemented.Result: For clinical studies, 4 trials with 201 patients in the minocycline group, and 195 patients in the control group met the inclusion criteria; 3 were randomized trials. At the end of 90-day follow up or discharge day, results showed that the groups receiving minocycline were superior to the control group, with significant differences in the NIHSS scores (mean difference [MD], −2.75; 95% CI, −4.78, 0.27; p = 0.03) and mRS scores (MD, −0.98; 95% CI, −1.27, −0.69; p < 0.01), but not Barthel Index Score (MD, 9.04; 95% CI, −0.78, 18.07; p = 0.07). For rodent experiments, 14 studies were included. Neurological severity scores (NSS) was significantly improved (MD, −1.38; 95% CI, −1.64, −1.31; p < 0.01) and infarct volume was obviously reduced (Std mean difference [SMD], −2.38; 95% CI, −3.40, −1.36; p < 0.01) in the minocycline group. Heterogeneity among the studies was proved to exist for infarct volume (Chi2 = 116.12, p < 0.01; I2 = 0.89) but not for other variables.Conclusions: Based on the results in our study, minocycline appears as an effective therapeutic option for acute ischemic stroke

ISSA-enhanced GRU-Transformer: integrating sports wisdom into the frontier exploration of carbon emission prediction

IntroductionCarbon neutrality has become a key strategy to combat global climate change. However, current methods for predicting carbon emissions are limited and require the development of more effective strategies to meet this challenge. This is especially true in the field of sports and competitions, where the energy intensity of major events and activities means that time series data is crucial for predicting related carbon emissions, as it can detail the emission patterns over a period of time.MethodIn this study, we introduce an artificial intelligence-based method aimed at improving the accuracy and reliability of carbon emission predictions. Specifically, our model integrates an Improved Mahjong Search Algorithm (ISSA) and GRU-Transformer technology, designed to efficiently process and analyze the complex time series data generated by sporting events. These technological components help to capture and parse carbon emission data more accurately.ResultsExperimental results have demonstrated the efficiency of our model, which underwent a comprehensive evaluation involving multiple datasets and was benchmarked against competing models. Our model outperformed others across various performance metrics, including lower RMSE and MAE values and higher R2 scores. This underscores the significant potential of our model in enhancing the accuracy of carbon emission predictions.DiscussionBy introducing this new AI-based method for predicting carbon emissions, this study not only provides more accurate data support for optimizing and implementing carbon neutrality measures in the sports field but also improves the accuracy of time series data predictions. This enables a deeper understanding of carbon emission trends associated with sports activities. It contributes to the development of more effective mitigation strategies, making a significant contribution to global efforts to reduce carbon emissions

Research progress on rheumatoid arthritis-associated depression

Depression is an independent mood disorder and one of the most common comorbidities of rheumatoid arthritis (RA). Growing evidence suggests that there is two-way regulation between RA and depression, resulting in a vicious cycle of RA, depression, poor outcomes, and disease burden. The rising prevalence of RA-associated depression warrants a re-examination of the relationships between them. Here we provide an overview of the etiology and pathological mechanisms of RA-associated depression, and recent advances in treatment with biologics, which will facilitate the development of new and effective prevention and treatment strategies