Borate-Functionalized Disiloxane as Effective Electrolyte Additive for 4.5 V LiNi0.8Co0.1Mn0.1O2/Graphite Batteries.

Ni-rich LiNi0.8Co0.1Mn0.1O2 (NCM811) is considered the most prominent cathode material to establish a practical high energy density of lithium-ion batteries (LIBs) for future electric vehicles. The energy density of LIBs is greatly determined by the capacity of electrode materials and the operating voltage of the cells. To further improve the cycle lifespan of NCM811 batteries to meet the requirement of driving range for the electric vehicle market, it is vital to design a novel electrolyte additive that can enhance the stability of the cathode/electrolyte interface at a wide range of voltage. Herein, a novel borate functionalized disiloxane compound, 1,1,1,3,3-pentamethyl-3-(3-(4,4,5,5-tetramethyl-1,3,2-dioxaborolan-2-yl)propyl) disiloxane (PMBPDS), is synthesized as cathode electrolyte interphase (CEI) film-forming additive to improve the cycling performance of NCM811 batteries. Systematic studies reveal that PMBPDS can construct a stable CEI film on the NCM811 surface and efficiently scavenge hydrofluoric acid (HF). The PMBPDS-derived CEI prevents the dissolution of transmission metals in the NCM811 cathode and enhances the capacity retention of NCM811/graphite cells from 68.3 to 70.6% after 200 cycles at 1 C in the voltage window of 3-4.5 V. This work provides more understanding on designing the molecular structure of additive compounds for improving the electrochemical performance of LIBs.

An approach for collaborative development of a federated biomedical knowledge graph-based question-answering system: Question-of-the-Month challenges.

Knowledge graphs have become a common approach for knowledge representation. Yet, the application of graph methodology is elusive due to the sheer number and complexity of knowledge sources. In addition, semantic incompatibilities hinder efforts to harmonize and integrate across these diverse sources. As part of The Biomedical Translator Consortium, we have developed a knowledge graph-based question-answering system designed to augment human reasoning and accelerate translational scientific discovery: the Translator system. We have applied the Translator system to answer biomedical questions in the context of a broad array of diseases and syndromes, including Fanconi anemia, primary ciliary dyskinesia, multiple sclerosis, and others. A variety of collaborative approaches have been used to research and develop the Translator system. One recent approach involved the establishment of a monthly "Question-of-the-Month (QotM) Challenge" series. Herein, we describe the structure of the QotM Challenge; the six challenges that have been conducted to date on drug-induced liver injury, cannabidiol toxicity, coronavirus infection, diabetes, psoriatic arthritis, and ATP1A3-related phenotypes; the scientific insights that have been gleaned during the challenges; and the technical issues that were identified over the course of the challenges and that can now be addressed to foster further development of the prototype Translator system. We close with a discussion on Large Language Models such as ChatGPT and highlight differences between those models and the Translator system.

Recent advances in targeting leucine-rich repeat kinase 2 as a potential strategy for the treatment of Parkinson's disease.

Parkinson's disease (PD) is the second most common neurodegenerative disease. Several single gene mutations involved in PD have been identified such as leucine-rich repeat kinase 2 (LRRK2), the most common cause of sporadic and familial PD. Its mutations have attracted much attention to therapeutically targeting this kinase. To date, many compounds including small chemical molecules with diverse scaffolds and RNA agents have been developed with significant amelioration in preclinical PD models. Currently, five candidates, DNL201, DNL151, WXWH0226, NEU-723 and BIIB094, have advanced to clinical trials for PD treatment. In this review, we describe the structure, pathogenic mutations and the mechanism of LRRK2, and summarize the development of LRRK2 inhibitors in preclinical and clinical studies, trying to provide an insight into targeting LRRK2 for PD intervention in future.

A Buried Thermal Rail (BTR) Technology to Improve Electrothermal Characteristics of Complementary Field-Effect Transistor (CFET).

The complementary field-effect transistor (CFET) with N-type FET (NFET) stacked on P-type FET (PFET) is a promising device structure based on gate-all-around FET (GAAFET). Because of the high-density stacked structure, the self-heating effect (SHE) becomes more and more severe. Buried thermal rail (BTR) technology on top of the buried power rail (BPR) process is proposed to improve heat dissipation. Through a systematical 3D Technology Computer Aided Design (TCAD) simulation, compared to traditional CFET and CFET with BPR only, the thermal resistance (Rth) of CFET can be significantly reduced with BTR technology, while the drive capability is also improved. Furthermore, based on the proposed BTR technology, different power delivery structures of top-VDD-top-VSS (TDTS), bottom-VDD-bottom-VSS (BDBS), and bottom-VDD-top-VSS (BDTS) were investigated in terms of electrothermal and parasitic characteristics. The Rth of the BTR-BDTS structure is decreased by 5% for NFET and 9% for PFET, and the Ion is increased by 2% for NFET and 7% for PFET.

BioThings Explorer: a query engine for a federated knowledge graph of biomedical APIs.

SUMMARY: Knowledge graphs are an increasingly common data structure for representing biomedical information. These knowledge graphs can easily represent heterogeneous types of information, and many algorithms and tools exist for querying and analyzing graphs. Biomedical knowledge graphs have been used in a variety of applications, including drug repurposing, identification of drug targets, prediction of drug side effects, and clinical decision support. Typically, knowledge graphs are constructed by centralization and integration of data from multiple disparate sources. Here, we describe BioThings Explorer, an application that can query a virtual, federated knowledge graph derived from the aggregated information in a network of biomedical web services. BioThings Explorer leverages semantically precise annotations of the inputs and outputs for each resource, and automates the chaining of web service calls to execute multi-step graph queries. Because there is no large, centralized knowledge graph to maintain, BioThings Explorer is distributed as a lightweight application that dynamically retrieves information at query time. AVAILABILITY AND IMPLEMENTATION: More information can be found at https://explorer.biothings.io and code is available at https://github.com/biothings/biothings_explorer.

Dry torrefaction and continuous thermochemical conversion for upgrading agroforestry waste into eco-friendly energy carriers: Current progress and future prospect.

Agroforestry Waste (AW) is seen as a carbon neutral resource. However, the poor quality of AW reduced its potential application value. Even more unfortunately, chlorine in AW led to the formation of organic pollutants such as dioxins under higher temperatures. Alkali and alkaline earth metals (AAEMs) in ash may deepen the reaction degree. Co-pretreatment of dry torrefaction and de-ashing followed by thermochemical conversion is a promising technology, which can improve raw material quality, inhibit the release of organic pollutants and transform AW into eco-friendly energy carriers. In order to better understand the process, theoretical basis such as the structural characteristics, thermal properties and separation methods of structural components of AW are described in detail. In addition, dry torrefaction related reactors, process parameters, kinetic analysis models as well as the evaluation methods of torrefaction degree and environmental impact are systematically reviewed. The problem of ash accumulation caused by dry torrefaction can be well solved by de-ashing pretreatment. This paper provides a comprehensive discussion on the role of the two- and three-stage conversion technologies around dry torrefacion, de-ashing pretreatment and thermochemical conversion in products quality enhancement. Finally, the existing technical challenges, including suppression of gaseous pollutant release, harmless treatment and reuse of torrefaction liquid product (TPL) and reduction of torrefaction operating costs, are summarized and evaluated. The future research directions, such as vitrification of the reused TPL (after de-ashing or acid catalysis) and integration of oxidative torrefaction with thermochemical conversion technologies, are proposed.

A Novel Atomic-Level Post-Etch-Surface-Reinforcement Process for High-Performance p-GaN Gate HEMTs Fabrication.

A novel atomic-level post-etch-surface-reinforcement (PESR) process is developed to recover the p-GaN etching induced damage region for high performance p-GaN gate HEMTs fabrication. This process is composed of a self-limited surface modification step with O2 plasma, following by an oxide removal step with BCl3 plasma. With PESR process, the AlGaN surface morphology after p-GaN etching was comparable to the as-epitaxial level by AFM characterization, and the AlGaN lattice crystallization was also recovered which was measured in a confocal Raman system. The electrical measurement further confirmed the significant improvement of AlGaN surface quality, with one-order of magnitude lower surface leakage in a metal-semiconductor (MS) Schottky-diode and 6 times lower interface density of states (Dit) in a MIS C-V characterization. The XPS analysis of Al2O3/AlGaN showed that the p-GaN etching induced F-byproduct and Ga-oxide was well removed and suppressed by PESR process. Finally, the developed PESR process was successfully integrated in p-GaN gate HEMTs fabrication, and the device performance was significantly enhanced with ~20% lower of on-resistance and ~25% less of current collapse at Vds,Q bias of 40 V, showing great potential of leverage p-GaN gate HEMTs reliability.

Non-Buffer Epi-AlGaN/GaN on SiC for High-Performance Depletion-Mode MIS-HEMTs Fabrication.

A systematic study of epi-AlGaN/GaN on a SiC substrate was conducted through a comprehensive analysis of material properties and device performance. In this novel epitaxial design, an AlGaN/GaN channel layer was grown directly on the AlN nucleation layer, without the conventional doped thick buffer layer. Compared to the conventional epi-structures on the SiC and Si substrates, the non-buffer epi-AlGaN/GaN structure had a better crystalline quality and surface morphology, with reliable control of growth stress. Hall measurements showed that the novel structure exhibited comparable transport properties to the conventional epi-structure on the SiC substrate, regardless of the buffer layer. Furthermore, almost unchanged carrier distribution from room temperature to 150 °C indicated excellent two-dimensional electron gas (2DEG) confinement due to the pulling effect of the conduction band from the nucleation layer as a back-barrier. High-performance depletion-mode MIS-HEMTs were demonstrated with on-resistance of 5.84 Ω·mm and an output current of 1002 mA/mm. The dynamic characteristics showed a much smaller decrease in the saturation current (only ~7%), with a quiescent drain bias of 40 V, which was strong evidence of less electron trapping owing to the high-quality non-buffer AlGaN/GaN epitaxial growth.

In Situ H-Radical Surface Treatment on Aluminum Gallium Nitride for High-Performance Aluminum Gallium Nitride/Gallium Nitride MIS-HEMTs Fabrication.

In this work, we demonstrated a low current collapse normally on Al2O3/AlGaN/GaN MIS-HEMT with in situ H-radical surface treatment on AlGaN. The in situ atomic pretreatment was performed in a specially designed chamber prior to the thermal ALD-Al2O3 deposition, which improved the Al2O3/AlGaN interface with Dit of ~2 × 1012 cm-2 eV-1, and thus effectively reduced the current collapse and the dynamic Ron degradation. The devices showed good electrical performance with low Vth hysteresis and peak trans-conductance of 107 mS/mm. Additionally, when the devices operated under 25 °C pulse-mode stress measurement with VDS,Q = 40 V (period of 1 ms, pulse width of 1 µs), the dynamic Ron increase of ~14.1% was achieved.

A Novel Scheme for Full Bottom Dielectric Isolation in Stacked Si Nanosheet Gate-All-Around Transistors.

In this paper, a novel scheme for source/drain-first (S/D-first) full bottom dielectric isolation (BDI), i.e., Full BDI_Last, with integration of a sacrificial Si0.5Ge0.5 layer was proposed and demonstrated in a stacked Si nanosheet gate-all-around (NS-GAA) device structure using TCAD simulations. The proposed full BDI scheme flow is compatible with the main process flow of NS-GAA transistor fabrication and provides a large window for process fluctuations, such as the thickness of the S/D recess. It is an ingenious solution to insert the dielectric material under the source, drain and gate regions to remove the parasitic channel. Moreover, because the S/D-first scheme decreases the problem of high-quality S/D epitaxy, the innovative fabrication scheme introduces full BDI formation after S/D epitaxy to mitigate the difficulty of providing stress engineering in the full BDI formation before S/D epitaxy (Full BDI_First). The electrical performance of Full BDI_Last is demonstrated by a 4.78-fold increase in the drive current compared to Full BDI_First. Furthermore, compared to traditional punch through stoppers (PTSs), the proposed Full BDI_Last technology could potentially provide an improved short channel behavior and good immunity against parasitic gate capacitance in NS-GAA devices. For the assessed inverter ring oscillator (RO), applying the Full BDI_Last scheme allows the operating speed to be increased by 15.2% and 6.2% at the same power, or alternatively enables an 18.9% and 6.8% lower power consumption at the same speed compared with the PTS and Full BDI_First schemes, respectively. The observations confirm that the novel Full BDI_Last scheme incorporated into an NS-GAA device can be utilized to enable superior characteristics to benefit the performance of integrated circuits.

BioThings Explorer: a query engine for a federated knowledge graph of biomedical APIs.

Knowledge graphs are an increasingly common data structure for representing biomedical information. These knowledge graphs can easily represent heterogeneous types of information, and many algorithms and tools exist for querying and analyzing graphs. Biomedical knowledge graphs have been used in a variety of applications, including drug repurposing, identification of drug targets, prediction of drug side effects, and clinical decision support. Typically, knowledge graphs are constructed by centralization and integration of data from multiple disparate sources. Here, we describe BioThings Explorer, an application that can query a virtual, federated knowledge graph derived from the aggregated information in a network of biomedical web services. BioThings Explorer leverages semantically precise annotations of the inputs and outputs for each resource, and automates the chaining of web service calls to execute multi-step graph queries. Because there is no large, centralized knowledge graph to maintain, BioThing Explorer is distributed as a lightweight application that dynamically retrieves information at query time. More information can be found at https://explorer.biothings.io, and code is available at https://github.com/biothings/biothings_explorer.

MyGeneset.info: an interactive and programmatic platform for community-curated and user-created collections of genes.

Gene definitions and identifiers can be painful to manage-more so when trying to include gene function annotations as this can be highly context-dependent. Creating groups of genes or gene sets can help provide such context, but it compounds the issue as each gene within the gene set can map to multiple identifiers and have annotations derived from multiple sources. We developed MyGeneset.info to provide an API for integrated annotations for gene sets suitable for use in analytical pipelines or web servers. Leveraging our previous work with MyGene.info (a server that provides gene-centric annotations and identifiers), MyGeneset.info addresses the challenge of managing gene sets from multiple resources. With our API, users readily have read-only access to gene sets imported from commonly-used resources such as Wikipathways, CTD, Reactome, SMPDB, MSigDB, GO, and DO. In addition to supporting the access and reuse of approximately 180k gene sets from humans, common model organisms (mice, yeast, etc.), and less-common ones (e.g. black cottonwood tree), MyGeneset.info supports user-created gene sets, providing an important means for making gene sets more FAIR. User-created gene sets can serve as a way to store and manage collections for analysis or easy dissemination through a consistent API.

Schema Playground: a tool for authoring, extending, and using metadata schemas to improve FAIRness of biomedical data.

BACKGROUND: Biomedical researchers are strongly encouraged to make their research outputs more Findable, Accessible, Interoperable, and Reusable (FAIR). While many biomedical research outputs are more readily accessible through open data efforts, finding relevant outputs remains a significant challenge. Schema.org is a metadata vocabulary standardization project that enables web content creators to make their content more FAIR. Leveraging Schema.org could benefit biomedical research resource providers, but it can be challenging to apply Schema.org standards to biomedical research outputs. We created an online browser-based tool that empowers researchers and repository developers to utilize Schema.org or other biomedical schema projects. RESULTS: Our browser-based tool includes features which can help address many of the barriers towards Schema.org-compliance such as: The ability to easily browse for relevant Schema.org classes, the ability to extend and customize a class to be more suitable for biomedical research outputs, the ability to create data validation to ensure adherence of a research output to a customized class, and the ability to register a custom class to our schema registry enabling others to search and re-use it. We demonstrate the use of our tool with the creation of the Outbreak.info schema-a large multi-class schema for harmonizing various COVID-19 related resources. CONCLUSIONS: We have created a browser-based tool to empower biomedical research resource providers to leverage Schema.org classes to make their research outputs more FAIR.

Impact of Stress and Dimension on Nanosheet Deformation during Channel Release of Gate-All-Around Device.

In this paper, nanosheet deformation during channel release has been investigated and discussed in Gate-All-Around (GAA) transistors. Structures with different source/drain size and stacked Si nanosheet lengths were designed and fabricated. The experiment of channel release showed that the stress caused serious deformation to suspended nanosheets. With the guidance of the experiment result, based on simulation studies using the COMSOL Multiphysics and Sentaurus tools, it is confirmed that the stress applied on the channel from source/drain plays an important role in nanosheet deformation during the fabrication process. The deformation of Si nanosheets would cause a serious degradation of the device performance due to an inability to control the work function of the metal gate. This study proposed that the uniformly stacked GAA nanosheets structure could be successfully demonstrated with suitable channel stress engineering provided by fitting S/D size and an appropriate channel length. The conclusions provide useful guidelines for future stacked GAA transistors' design and fabrication.

A Novel Si Nanosheet Channel Release Process for the Fabrication of Gate-All-Around Transistors and Its Mechanism Investigation.

The effect of the source/drain compressive stress on the mechanical stability of stacked Si nanosheets (NS) during the process of channel release has been investigated. The stress of the nanosheets in the stacking direction increased first and then decreased during the process of channel release by technology computer-aided design (TCAD) simulation. The finite element simulation showed that the stress caused serious deformation of the nanosheets, which was also confirmed by the experiment. This study proposed a novel channel release process that utilized multi-step etching to remove the sacrificial SiGe layers instead of conventional single-step etching. By gradually releasing the stress of the SiGe layer on the nanosheets, the stress difference in the stacking direction before and after the last step of etching was significantly reduced, thus achieving equally spaced stacked nanosheets. In addition, the plasma-free oxidation treatment was introduced in the multi-step etching process to realize an outstanding selectivity of 168:1 for Si0.7Ge0.3 versus Si. The proposed novel process could realize the channel release of nanosheets with a multi-width from 30 nm to 80 nm with little Si loss, unlocking the full potential of gate-all-around (GAA) technology for digital, analog, and radio-frequency (RF) circuit applications.

Developing a standardized but extendable framework to increase the findability of infectious disease datasets.

Biomedical datasets are increasing in size, stored in many repositories, and face challenges in FAIRness (findability, accessibility, interoperability, reusability). As a Consortium of infectious disease researchers from 15 Centers, we aim to adopt open science practices to promote transparency, encourage reproducibility, and accelerate research advances through data reuse. To improve FAIRness of our datasets and computational tools, we evaluated metadata standards across established biomedical data repositories. The vast majority do not adhere to a single standard, such as Schema.org, which is widely-adopted by generalist repositories. Consequently, datasets in these repositories are not findable in aggregation projects like Google Dataset Search. We alleviated this gap by creating a reusable metadata schema based on Schema.org and catalogued nearly 400 datasets and computational tools we collected. The approach is easily reusable to create schemas interoperable with community standards, but customized to a particular context. Our approach enabled data discovery, increased the reusability of datasets from a large research consortium, and accelerated research. Lastly, we discuss ongoing challenges with FAIRness beyond discoverability.

Outbreak.info Research Library: a standardized, searchable platform to discover and explore COVID-19 resources.

Outbreak.info Research Library is a standardized, searchable interface of coronavirus disease 2019 (COVID-19) and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) publications, clinical trials, datasets, protocols and other resources, built with a reusable framework. We developed a rigorous schema to enforce consistency across different sources and resource types and linked related resources. Researchers can quickly search the latest research across data repositories, regardless of resource type or repository location, via a search interface, public application programming interface (API) and R package.

Outbreak.info genomic reports: scalable and dynamic surveillance of SARS-CoV-2 variants and mutations.

In response to the emergence of SARS-CoV-2 variants of concern, the global scientific community, through unprecedented effort, has sequenced and shared over 11 million genomes through GISAID, as of May 2022. This extraordinarily high sampling rate provides a unique opportunity to track the evolution of the virus in near real-time. Here, we present outbreak.info , a platform that currently tracks over 40 million combinations of Pango lineages and individual mutations, across over 7,000 locations, to provide insights for researchers, public health officials and the general public. We describe the interpretable visualizations available in our web application, the pipelines that enable the scalable ingestion of heterogeneous sources of SARS-CoV-2 variant data and the server infrastructure that enables widespread data dissemination via a high-performance API that can be accessed using an R package. We show how outbreak.info can be used for genomic surveillance and as a hypothesis-generation tool to understand the ongoing pandemic at varying geographic and temporal scales.

Switchable and Highly Robust Ultralong Room-Temperature Phosphorescence from Polymer-Based Transparent Films with Three-Dimensional Covalent Networks for Erasable Light Printing.

In this work, an efficient polymer-based organic afterglow system, which shows reversible photochromism, switchable ultralong organic phosphorescence (UOP), and prominent water and chemical resistance simultaneously, has been developed for the first time. By doping phenoxazine (PXZ) and 10-ethyl-10H-phenoxazine (PXZEt) into epoxy polymers, the resulting PXZ@EP-0.25 % and PXZEt@EP-0.25 % films show unique photoactivated UOP properties, with phosphorescence quantum yields and lifetimes up to 10.8 % and 845 ms, respectively. It is found that the steady-state luminescence and UOP of PXZ@EP-0.25 % are switchable by light irradiation and thermal annealing. Moreover, the doped films can still produce conspicuous UOP after soaking in water, strong acid and base, and organic solvents for more than two weeks, exhibiting outstanding water and chemical resistance. Inspired by these exciting results, the PXZ@EP-0.25 % has been successfully exploited as an erasable transparent film for light printing.