Stepless IR Chromism in Ti3 C2 Tx MXene Tuned by Interlayer Water Molecules.

Real-time control over infrared (IR) radiation of objects is highly desired in a variety of areas such as personal thermal regulation and IR camouflage. This requires the dynamic modulation of IR emissivity in a stepless manner over a wide range (>50%), which remains a daunting challenge. Here, an emissivity modulation phenomenon is reported in stacked 2D Ti3 C2 Tx MXene nanosheets, from 12% to 68% as the intercalation/discharging of water molecules within the interlayers. The intercalation of water molecules dynamically changes the electronic properties and the complex permittivity in IR frequencies of Ti3 C2 Tx . This emissivity modulation is a stepless and reversible process without the assistance of any external energy input. Further, intercalating cellulose nanofibers into the Ti3 C2 Tx interlayers makes this dynamic process highly repeatable. Last, a sweat-responsive adaptive textile that can improve thermal comfort of human body under changes in metabolic rates and environmental conditions is demonstrated, showing great potential of this mechanism in passive on-demand radiation modulation.

A Highly Transparent Photo-Electro-Thermal Film with Broadband Selectivity for All-Day Anti-/De-Icing.

A photo- and electro-thermal film can convert sunlight and electricity into heat to solve icing problems. Combination of them provides an efficient strategy for all-day anti-/de-icing. However, only opaque surfaces have been reported, due to the mutual exclusiveness between photon absorption and transmission. Herein, a highly transparent and scalable solution-processed photo-electro-thermal film is reported, which exhibits an ultra-broadband selective spectrum to separate the visible light from sunlight and a countertrend suppress of emission in longer wavelength. It absorbs ≈ 85% of invisible sunlight (ultraviolet and near-infrared) for light-heat conversion, meanwhile maintains luminous transmittance > 70%. The reflection of mid-infrared leads to low emissivity (0.41), which further preserves heat on the surface for anti-/de-icing purpose. This ultra-broadband selectivity enables temperature elevation > 40 °C under 1-sun illumination and the mutual support between photo-thermal and electro-thermal effects contributes to > 50% saving of electrical consumption under weak solar exposure (0.4-sun) for maintaining unfrozen surfaces at -35 °C environment. The reverberation from photo-electro-thermal and super-hydrophobic effects illustrates a lubricating removal of grown ice in short time (< 120 s). The self-cleaning ability and the durability under mechanical, electrical, optical, and thermal stresses render the film stable for long-term usage in all-day anti-/de-icing applications.

A UV-Reflective Organic-Inorganic Tandem Structure for Efficient and Durable Daytime Radiative Cooling in Harsh Climates.

Radiative cooling shows great promise in eco-friendly space cooling due to its zero-energy consumption. For subambient cooling in hot humid subtropical/tropical climates, achieving ultrahigh solar reflectance (≥96%), durable ultraviolet (UV) resistance, and surface superhydrophobicity simultaneously is critical, which, however, is challenging for most state-of-the-art scalable polymer-based coolers. Here an organic-inorganic tandem structure is reported to address this challenge, which comprises a bottom high-refractive-index polyethersulfone (PES) cooling layer with bimodal honeycomb pores, an alumina (Al2 O3 ) nanoparticle UV reflecting layer with superhydrophobicity, and a middle UV absorption layer of titanium dioxide (TiO2 ) nanoparticles, thus providing thorough protection from UV and self-cleaning capability together with outstanding cooling performance. The PES-TiO2 -Al2 O3 cooler demonstrates a record-high solar reflectance of over 0.97 and high mid-infrared emissivity of 0.92, which can maintain their optical properties intact even after equivalent 280-day UV exposure despite the UV-sensitivity of PES. This cooler achieves a subambient cooling temperature up to 3 °C at summer noontime and 5 °C at autumn noontime without solar shading or convection cover in a subtropical coastal city, Hong Kong. This tandem structure can be extended to other polymer-based designs, offering a UV-resist but reliable radiative cooling solution in hot humid climates.

Chemical identification and indexing in full-text articles: an overview of the NLM-Chem track at BioCreative VII.

The BioCreative National Library of Medicine (NLM)-Chem track calls for a community effort to fine-tune automated recognition of chemical names in the biomedical literature. Chemicals are one of the most searched biomedical entities in PubMed, and-as highlighted during the coronavirus disease 2019 pandemic-their identification may significantly advance research in multiple biomedical subfields. While previous community challenges focused on identifying chemical names mentioned in titles and abstracts, the full text contains valuable additional detail. We, therefore, organized the BioCreative NLM-Chem track as a community effort to address automated chemical entity recognition in full-text articles. The track consisted of two tasks: (i) chemical identification and (ii) chemical indexing. The chemical identification task required predicting all chemicals mentioned in recently published full-text articles, both span [i.e. named entity recognition (NER)] and normalization (i.e. entity linking), using Medical Subject Headings (MeSH). The chemical indexing task required identifying which chemicals reflect topics for each article and should therefore appear in the listing of MeSH terms for the document in the MEDLINE article indexing. This manuscript summarizes the BioCreative NLM-Chem track and post-challenge experiments. We received a total of 85 submissions from 17 teams worldwide. The highest performance achieved for the chemical identification task was 0.8672 F-score (0.8759 precision and 0.8587 recall) for strict NER performance and 0.8136 F-score (0.8621 precision and 0.7702 recall) for strict normalization performance. The highest performance achieved for the chemical indexing task was 0.6073 F-score (0.7417 precision and 0.5141 recall). This community challenge demonstrated that (i) the current substantial achievements in deep learning technologies can be utilized to improve automated prediction accuracy further and (ii) the chemical indexing task is substantially more challenging. We look forward to further developing biomedical text-mining methods to respond to the rapid growth of biomedical literature. The NLM-Chem track dataset and other challenge materials are publicly available at https://ftp.ncbi.nlm.nih.gov/pub/lu/BC7-NLM-Chem-track/. Database URL https://ftp.ncbi.nlm.nih.gov/pub/lu/BC7-NLM-Chem-track/.

A Janus Textile Capable of Radiative Subambient Cooling and Warming for Multi-Scenario Personal Thermal Management.

Textiles with radiative cooling/warming capabilities provide a green and effective solution to personal thermal comfort in different climate scenarios. However, developing multiple-mode textiles for wearing in changing climates with large temperature variation remains a challenge. Here a Janus textile is reported, comprising a polyethersulfone (PES)-Al2 O3 cooling layer optically coupled with a Ti3 C2 Tx warming layer, which can realize sub-ambient radiative cooling, solar warming, and active Joule heating. Owing to the intrinsically high refractive index of PES and the rational design of the fiber topology, the nanocomposite PES textile features a record high solar reflectance of 0.97. Accompanied by an infrared (IR) emittance of 0.91 in the atmospheric window, sub-ambient cooling of 0.5-2.5 °C is achieved near noontime in humid summer under ≈1000 W m-2 solar irradiation in Hong Kong. The simulated skin covered with the textile is ≈10 °C cooler than that with white cotton. The Ti3 C2 Tx layer provides a high solar-thermal efficiency of ≈80% and a Joule heating flux of 66 W m-2 at 2 V and 15 °C due to its excellent spectral selectivity and electrical conductivity. The switchable multiple working modes enable effective and adaptive personal thermal management in changing environments.

BioKG: a comprehensive, large-scale biomedical knowledge graph for AI-powered, data-driven biomedical research.

To cope with the rapid growth of scientific publications and data in biomedical research, knowledge graphs (KGs) have emerged as a powerful data structure for integrating large volumes of heterogeneous data to facilitate accurate and efficient information retrieval and automated knowledge discovery (AKD). However, transforming unstructured content from scientific literature into KGs has remained a significant challenge, with previous methods unable to achieve human-level accuracy. In this study, we utilized an information extraction pipeline that won first place in the LitCoin NLP Challenge to construct a largescale KG using all PubMed abstracts. The quality of the large-scale information extraction rivals that of human expert annotations, signaling a new era of automatic, high-quality database construction from literature. Our extracted information markedly surpasses the amount of content in manually curated public databases. To enhance the KG's comprehensiveness, we integrated relation data from 40 public databases and relation information inferred from high-throughput genomics data. The comprehensive KG enabled rigorous performance evaluation of AKD, which was infeasible in previous studies. We designed an interpretable, probabilistic-based inference method to identify indirect causal relations and achieved unprecedented results for drug target identification and drug repurposing. Taking lung cancer as an example, we found that 40% of drug targets reported in literature could have been predicted by our algorithm about 15 years ago in a retrospective study, demonstrating that substantial acceleration in scientific discovery could be achieved through automated hypotheses generation and timely dissemination. A cloud-based platform (https://www.biokde.com) was developed for academic users to freely access this rich structured data and associated tools.

A reinforcement learning approach for protein-ligand binding pose prediction.

Protein ligand docking is an indispensable tool for computational prediction of protein functions and screening drug candidates. Despite significant progress over the past two decades, it is still a challenging problem, characterized by the still limited understanding of the energetics between proteins and ligands, and the vast conformational space that has to be searched to find a satisfactory solution. In this project, we developed a novel reinforcement learning (RL) approach, the asynchronous advantage actor-critic model (A3C), to address the protein ligand docking problem. The overall framework consists of two models. During the search process, the agent takes an action selected by the actor model based on the current location. The critic model then evaluates this action and predict the distance between the current location and true binding site. Experimental results showed that in both single- and multi-atom cases, our model improves binding site prediction substantially compared to a naïve model. For the single-atom ligand, copper ion (Cu2+), the model predicted binding sites have a median root-mean-square-deviation (RMSD) of 2.39 Å to the true binding sites when starting from random starting locations. For the multi-atom ligand, sulfate ion (SO42-), the predicted binding sites have a median RMSD of 3.82 Å to the true binding sites. The ligand-specific models built in this study can be used in solvent mapping studies and the RL framework can be readily scaled up to larger and more diverse sets of ligands.

Pre-trained models, data augmentation, and ensemble learning for biomedical information extraction and document classification.

Large volumes of publications are being produced in biomedical sciences nowadays with ever-increasing speed. To deal with the large amount of unstructured text data, effective natural language processing (NLP) methods need to be developed for various tasks such as document classification and information extraction. BioCreative Challenge was established to evaluate the effectiveness of information extraction methods in biomedical domain and facilitate their development as a community-wide effort. In this paper, we summarize our work and what we have learned from the latest round, BioCreative Challenge VII, where we participated in all five tracks. Overall, we found three key components for achieving high performance across a variety of NLP tasks: (1) pre-trained NLP models; (2) data augmentation strategies and (3) ensemble modelling. These three strategies need to be tailored towards the specific tasks at hands to achieve high-performing baseline models, which are usually good enough for practical applications. When further combined with task-specific methods, additional improvements (usually rather small) can be achieved, which might be critical for winning competitions. Database URL: https://doi.org/10.1093/database/baac066.

Nanoparticle-on-Mirror Metamaterials for Full-Spectrum Selective Solar Energy Harvesting.

Most broadband metamaterial absorbers are realized by patterning periodic arrays of plasmonic nanoparticles (>100 nm) on dielectric/metallic substrates to enable both electric and magnetic resonances. These metamaterials, however, require costly nanolithographic top-down techniques for fabrication. Here, we demonstrate new-concept nanoparticle-on-mirror (NoM) metamaterial absorbers by densely packing plasmonic nanoparticles of much smaller size (∼30 nm) on metal films directly. Such a simple but rational design enables the use of all-solution-based bottom-up processes. Because of the decoupling of electric and magnetic polarizations in these ultrasmall nanoparticles, excellent impedance match and near-perfect light absorption can be achieved in a broad band over the solar spectrum with weak thermal emission. Proof-of-concept large-area NoM metamaterial absorbers that offer a solar absorptance of 94% but a low IR emittance of 2% are experimentally demonstrated. The outstanding performance, bottom-up process, and great compatibility render the design promising for efficient and large-scale solar energy harvesting.