Multiarray Gas Sensors Using Ternary Combined Ti3C2Tx MXene-Based Nanocomposites.

This paper reports chemiresistive multiarray gas sensors through the synthesized ternary nanocomposites, using a one-pot method to integrate two-dimensional MXene (Ti3C2Tx) with Ti-doped WO3 (Ti-WO3/Ti3C2Tx) and Ti3C2Tx with Pd-doped SnO2 (Pd-SnO2/Ti3C2Tx). The gas sensors based on Ti-WO3/Ti3C2Tx and Pd-SnO2/Ti3C2Tx exhibit exceptional sensitivity, particularly in detecting 70% at 1 ppm acetone and 91.1% at 1 ppm of H2S. Notably, our sensors demonstrate a remarkable sensing performance in the low-ppb range for acetone and H2S. Specifically, the Ti-WO3/Ti3C2Tx sensor demonstrates a detection limit of 0.035 ppb for acetone, and the Pd-SnO2/Ti3C2Tx sensor shows 0.116 ppb for H2S. Simultaneous measurements with Ti-WO3/Ti3C2Tx- and Pd-SnO2/Ti3C2Tx-based sensors enable the evaluation of both the concentration and type of unknown target gases, such as acetone or H2S. Furthermore, density functional theory calculations are performed to clarify the role of Ti and Pd doping in enhancing the performance of Ti-WO3/Ti3C2Tx and Pd-SnO2/Ti3C2Tx nanocomposites. Theoretical simulations contribute to a deeper understanding of the doping effects, providing essential insights into the mechanisms underlying the enhanced gas response of the gas sensors. Overall, this work provides valuable insights into the gas-sensing mechanisms and introduces a novel approach for high-performance multiarray gas sensing.

High Glass Transition Temperature Fluorinated Polymers Based on Transfer Learning with Small Experimental Data.

Machine learning can be used to predict the properties of polymers and explore vast chemical spaces. However, the limited number of available experimental datasets hinders the enhancement of the predictive performance of a model. This study proposes a machine learning approach that leverages transfer learning and ensemble modeling to efficiently predict the glass transition temperature (Tg) of fluorinated polymers and guide the design of high Tg copolymers. Initially, the quantum machine 9 (QM9) dataset is employed for model pretraining, thus providing robust molecular representations for the subsequent fine-tuning of a specialized copolymer dataset. Ensemble modeling is used to further enhance prediction robustness and reliability, effectively addressing the problems owing to the limited and unevenly distributed nature of the copolymer dataset. Finally, a fine-tuned ensemble model is used to navigate a vast chemical space comprising 61 monomers and identify promising candidates for high Tg fluorinated polymers. The model predicts 247 entries capable of achieving a Tg over 390 K, of which 14 are experimentally validated. This study demonstrates the potential of machine learning in material design and discovery, highlighting the effectiveness of transfer learning and ensemble modeling strategies for overcoming the challenges posed by small datasets in complex copolymer systems.

Room Temperature Reduction of Nitrogen Oxide on Iron Metal-Organic Frameworks.

Nitrogen oxides represent one of the main threats for the environment. Despite decades of intensive research efforts, a sustainable solution for NOx removal under environmental conditions is still undefined. Using theoretical modelling, material design, state-of-the-art investigation methods and mimicking enzymes, it is found that selected porous hybrid iron(II/III) based MOF material are able to decompose NOx, at room temperature, in the presence of water and oxygen, into N2 and O2 and without reducing agents. This paves the way to the development of new highly sustainable heterogeneous catalysts to improve air quality.

Optical property dataset of inorganic phosphor.

Developing inorganic phosphor with desired properties for light-emitting diode application has traditionally relied on time-consuming and labor-intensive material development processes. Moreover, the results of material development research depend significantly on individual researchers' intuition and experience. Thus, to improve the efficiency and reliability of materials discovery, machine learning has been widely applied to various materials science applications in recent years. However, the prediction capabilities of machine learning methods fundamentally depend on the quality of the training datasets. In this work, we constructed a high-quality and reliable dataset that contains experimentally validated inorganic phosphors and their optical properties, sourced from the literature on inorganic phosphors. Our dataset includes 3952 combinations of 21 dopant elements in 2238 host materials from 553 articles. The dataset provides material information, optical properties, measurement conditions for inorganic phosphors, and meta-information. Among the preliminary machine learning results, the essential properties of inorganic phosphors, such as maximum Photoluminescence (PL) emission wavelength and PL decay time, show overall satisfactory prediction performance with coefficient of determination ( R 2 ) scores of 0.7 or more. We also confirmed that the measurement conditions significantly improved prediction performance.

Investigation of Laser-Induced Graphene (LIG) on a Flexible Substrate and Its Functionalization by Metal Doping for Gas-Sensing Applications.

Graphene materials synthesized using direct laser writing (laser-induced graphene; LIG) make favorable sensor materials because of their large surface area, ease of fabrication, and cost-effectiveness. In particular, LIG decorated with metal nanoparticles (NPs) has been used in various sensors, including chemical sensors and electronic and electrochemical biosensors. However, the effect of metal decoration on LIG sensors remains controversial; hypotheses based on computational simulations do not always match the experimental results, and even the experimental results reported by different researchers have not been consistent. In the present study, we explored the effects of metal decorations on LIG gas sensors, with NO2 and NH3 gases as the representative oxidizing and reducing agents, respectively. To eliminate the unwanted side effects arising from metal salt residues, metal NPs were directly deposited via vacuum evaporation. Although the gas sensitivities of the sensors deteriorate upon metal decoration irrespective of the metal work function, in the case of NO2 gas, they improve upon metal decoration in the case of NH3 exposure. A careful investigation of the chemical structure and morphology of the metal NPs in the LIG sensors shows that the spontaneous oxidation of metal NPs with a low work function changes the behavior of the LIG gas sensors and that the sensors' behaviors under NO2 and NH3 gases follow different principles.

Unusual Raman Enhancement Effect of Ultrathin Copper Sulfide.

In surface-enhanced Raman spectroscopy (SERS), 2D materials are explored as substrates owing to their chemical stability and reproducibility. However, they exhibit lower enhancement factors (EFs) compared to noble metal-based SERS substrates. This study demonstrates the application of ultrathin covellite copper sulfide (CuS) as a cost-effective SERS substrate with a high EF value of 7.2 × 104 . The CuS substrate is readily synthesized by sulfurizing a Cu thin film at room temperature, exhibiting a Raman signal enhancement comparable to that of an Au noble metal substrate of similar thickness. Furthermore, computational simulations using the density functional theory are employed and time-resolved photoluminescence measurements are performed to investigate the enhancement mechanisms. The results indicate that polar covalent bonds (Cu─S) and strong interlayer interactions in the ultrathin CuS substrate increase the probability of charge transfer between the analyte molecules and the CuS surface, thereby producing enhanced SERS signals. The CuS SERS substrate demonstrates the selective detection of various dye molecules, including rhodamine 6G, methylene blue, and safranine O. Furthermore, the simplicity of CuS synthesis facilitates large-scale production of SERS substrates with high spatial uniformity, exhibiting a signal variation of less than 5% on a 4-inch wafer.

Data-Driven Enhancement of ZT in SnSe-Based Thermoelectric Systems.

Doping and alloying are fundamental strategies to improve the thermoelectric performance of bare materials. However, identifying outstanding elements and compositions for the development of high-performance thermoelectric materials is challenging. In this study, we present a data-driven approach to improve the thermoelectric performance of SnSe compounds with various doping. Based on the newly generated experimental and computational dataset, we built highly accurate predictive models of thermoelectric properties of doped SnSe compounds. A well-designed feature vector consisting of the chemical properties of a single atom and the electronic structures of a solid plays a key role in achieving accurate predictions for unknown doping elements. Using the machine learning predictive models and calculated map of the solubility limit for each dopant, we rapidly screened high-dimensional material spaces of doped SnSe and evaluated their thermoelectric properties. This data-driven search provided overall strategies to optimize and improve the thermoelectric properties of doped SnSe compounds. In particular, we identified five dopant candidate elements (Ge, Pb, Y, Cd, and As) that provided a high ZT exceeding 2.0 and proposed a design principle for improving the ZT by Sn vacancies depending on the doping elements. Based on the search, we proposed yttrium as a new high-ZT dopant for SnSe with experimental confirmations. Our research is expected to lead to novel high-ZT thermoelectric material candidates and provide cutting-edge research strategies for materials design and extraction of design principles through data-driven research.

Nonlinearity encoding to improve extrapolation capabilities for unobserved physical states.

The fundamental goal of machine learning (ML) in physical science is to predict the physical properties of unobserved states. However, an accurate prediction for input data outside of training distributions is a challenging problem in ML due to the nonlinearities in input and target dynamics. For an accurate extrapolation of ML algorithms, we propose a new data-driven method that encodes the nonlinearities of physical systems into input representations. Based on the proposed encoder, a given physical system is described as linear-like functions that are easy to extrapolate. By applying the proposed encoder, the extrapolation errors were significantly reduced by 48.39% and 40.04% in n-body problem and materials property prediction, respectively.

Electrode-Induced Self-Healed Monolayer MoS2 for High Performance Transistors and Phototransistors.

Contact engineering for monolayered transition metal dichalcogenides (TMDCs) is considered to be of fundamental challenge for realizing high-performance TMDCs-based (opto) electronic devices. Here, an innovative concept is established for a device configuration with metallic copper monosulfide (CuS) electrodes that induces sulfur vacancy healing in the monolayer molybdenum disulfide (MoS2 ) channel. Excess sulfur adatoms from the metallic CuS electrodes are donated to heal sulfur vacancy defects in MoS2 that surprisingly improve the overall performance of its devices. The electrode-induced self-healing mechanism is demonstrated and analyzed systematically using various spectroscopic analyses, density functional theory (DFT) calculations, and electrical measurements. Without any passivation layers, the self-healed MoS2 (photo)transistor with the CuS contact electrodes show outstanding room temperature field effect mobility of 97.6 cm2 (Vs)-1 , On/Off ratio > 108 , low subthreshold swing of 120 mV per decade, high photoresponsivity of 1 × 104  A W-1 , and detectivity of 1013 jones, which are the best among back-gated transistors that employ 1L MoS2 . Using ultrathin and flexible 2D CuS and MoS2 , mechanically flexible photosensor is also demonstrated, which shows excellent durability under mechanical strain. These findings demonstrate a promising strategy in TMDCs or other 2D material for the development of high performance and functional devices including self-healable sulfide electrodes.

Tuplewise Material Representation Based Machine Learning for Accurate Band Gap Prediction.

The open-access material databases allowed us to approach scientific questions from a completely new perspective with machine learning methods. Here, on the basis of open-access databases, we focus on the classical band gap problem for predicting accurately the band gap of a crystalline compound using a machine learning approach with newly developed tuplewise graph neural networks (TGNN), which is devised to automatically generate input representation of crystal structures in tuple types and to exploit crystal-level properties as one of the input features. Our method brings about a highly accurate prediction of the band gaps at hybrid functionals and GW approximation levels for multiple material data sets without heavy computational cost. Furthermore, to demonstrate the applicability of our prediction model, we provide a data set of GW band gaps for 45835 materials predicted by TGNN posing higher accuracy than standard density functional theory calculations.

Near-infrared-emitting nanoparticles activate collagen synthesis via TGFß signaling.

Research efforts towards developing near-infrared (NIR) therapeutics to activate the proliferation of human keratinocytes and collagen synthesis in the skin microenvironment have been minimal, and the subject has not been fully explored. Herein, we describe the novel synthesis Ag2S nanoparticles (NPs) by using a sonochemical method and reveal the effects of NIR irradiation on the enhancement of the production of collagen through NIR-emitting Ag2S NPs. We also synthesized Li-doped Ag2S NPs that exhibited significantly increased emission intensity because of their enhanced absorption ability in the UV-NIR region. Both Ag2S and Li-doped Ag2S NPs activated the proliferation of HaCaT (human keratinocyte) and HDF (human dermal fibroblast) cells with no effect on cell morphology. While Ag2S NPs upregulated TIMP1 by only twofold in HaCaT cells and TGF-ß1 by only fourfold in HDF cells, Li-doped Ag2S NPs upregulated TGF-ß1 by tenfold, TIMP1 by 26-fold, and COL1A1 by 18-fold in HaCaT cells and upregulated TGF-ß1 by fivefold and COL1A1 by fourfold in HDF cells. Furthermore, Ag2S NPs activated TGF-ß1 signaling by increasing the phosphorylation of Smad2 and Smad3. The degree of activation was notably higher in cells treated with Li-doped Ag2S NPs, mainly caused by the higher PL intensity from Li-doped Ag2S NPs. Ag2S NPs NIR activates cell proliferation and collagen synthesis in skin keratinocytes and HDF cells, which can be applied to clinical light therapy and the development of anti-wrinkle agents for cosmetics.

Tuning graphene transistors through ad hoc electrostatics induced by a nanometer-thick molecular underlayer.

We report on the modulation of the electrical properties of graphene-based transistors that mirror the properties of a few nanometers thick layer made of dipolar molecules sandwiched in between the 2D material and the SiO2 dielectric substrate. The chemical composition of the films of quinonemonoimine zwitterion molecules adsorbed onto SiO2 has been explored by means of X-ray photoemission and mass spectroscopy. Graphene-based devices are then fabricated by transferring the 2D material onto the molecular film, followed by the deposition of top source-drain electrodes. The degree of supramolecular order in disordered films of dipolar molecules was found to be partially improved as a result of the electric field at low temperatures, as revealed by the emergence of hysteresis in the transfer curves of the transistors. The use of molecules from the same family, which are suitably designed to interact with the dielectric surface, results in the disappearance of the hysteresis. DFT calculations confirm that the dressing of the molecules by an external electric field exhibits multiple minimal energy landscapes that explain the thermally stabilized capacitive coupling observed. This study demonstrates that the design and exploitation of ad hoc molecules as an interlayer between a dielectric substrate and graphene represents a powerful tool for tuning the electrical properties of the 2D material. Conversely, graphene can be used as an indicator of the stability of molecular layers, by providing insight into the energetics of ordering of dipolar molecules under the effect of electrical gating.

New red-emitting phosphor Rb x K3-x SiF7:Mn4+ (x = 0, 1, 2, 3): DFT predictions and synthesis.

Finding new phosphors through an efficient method is important in terms of saving time and cost related to the development of phosphor materials. The ability to identify new phosphors through preliminary simulations by calculations prior to the actual synthesis of the materials can maximize the efficiency of novel phosphor development. In this paper, we demonstrate the use of density functional theory (DFT) calculations to guide the development of a new red phosphor. We performed first-principles calculations based on DFT for pristine and Mn-doped Rb x K3-x SiF7 (x = 0, 1, 2, 3) and predicted their stability, electronic structure, and luminescence properties. On the basis of the results, we then synthesized the stable Rb2KSiF7:Mn4+ red conversion phosphor and investigated its luminescence, structure, and stability. As a result, we confirmed that Rb2KSiF7:Mn4+ emitted red light with a longer wavelength than that emitted by K3SiF7:Mn4+ and a wavelength similar to that of K2SiF6:Mn4+. These results show that DFT calculations can provide rational insights into the design of a phosphor material before it is synthesized, thereby reducing the time and cost required to develop new red conversion phosphors.

Ultrathin Metal Crystals: Growth on Supported Graphene Surfaces and Applications.

Controlled nucleation and growth of metal clusters in metal deposition processes is a long-standing issue for thin-film-based electronic devices. When metal atoms are deposited on solid surfaces, unintended defects sites always lead to a heterogeneous nucleation, resulting in a spatially nonuniform nucleation with irregular growth rates for individual nuclei, resulting in a rough film that requires a thicker film to be deposited to reach the percolation threshold. In the present study, it is shown that substrate-supported graphene promotes the lateral 2D growth of metal atoms on the graphene. Transmission electron microscopy reveals that 2D metallic single crystals are grown epitaxially on supported graphene surfaces while a pristine graphene layer hardly yields any metal nucleation. A surface energy barrier calculation based on density functional theory predicts a suppression of diffusion of metal atoms on electronically perturbed graphene (supported graphene). 2D single Au crystals grown on supported graphene surfaces exhibit unusual near-infrared plasmonic resonance, and the unique 2D growth of metal crystals and self-healing nature of graphene lead to the formation of ultrathin, semitransparent, and biodegradable metallic thin films that could be utilized in various biomedical applications.

Synthesis of silver sulfide nanoparticles and their photodetector applications.

Silver sulfide nanoparticles (Ag2S NPs) are currently being explored as infrared active nanomaterials that can provide environmentally stable alternatives to heavy metals such as lead. In this paper, we describe the novel synthesis of Ag2S NPs by using a sonochemistry method and the fabrication of photodetector devices through the integration of Ag2S NPs atop a graphene sheet. We have also synthesized Li-doped Ag2S NPs that exhibited a significantly enhanced photodetector sensitivity via their enhanced absorption ability in the UV-NIR region. First-principles calculations based on a density functional theory formalism indicated that Li-doping produced a dramatic enhancement of NIR photoluminescence of the Ag2S NPs. Finally, high-performance photodetectors based on CVD graphene and Ag2S NPs were demonstrated and investigated; the hybrid photodetectors based on Ag2S NPs and Li-doped Ag2S NPs exhibited a photoresponse of 2723.2 and 4146.0 A W-1 respectively under a light exposure of 0.89 mW cm-2 at 550 nm. Our novel approach represents a promising and effective method for the synthesis of eco-friendly semiconducting NPs for photoelectric devices.

Graphene as a thin-film catalyst booster: graphene-catalyst interface plays a critical role.

Due to its extreme thinness, graphene can transmit some surface properties of its underlying substrate, a phenomenon referred to as graphene transparency. Here we demonstrate the application of the transparency of graphene as a protector of thin-film catalysts and a booster of their catalytic efficiency. The photocatalytic degradation of dye molecules by ZnO thin films was chosen as a model system. A ZnO thin film coated with monolayer graphene showed greater catalytic efficiency and long-term stability than did bare ZnO. Interestingly, we found the catalytic efficiency of the graphene-coated ZnO thin film to depend critically on the nature of the bottom ZnO layer; graphene transferred to a relatively rough, sputter-coated ZnO thin film showed rather poor catalytic degradation of the dye molecules while a smooth sol-gel-synthesized ZnO covered with monolayer graphene showed enhanced catalytic degradation. Based on a systematic investigation of the interface between graphene and ZnO thin films, we concluded the transparency of graphene to be critically dependent on its interface with a supporting substrate. Graphene supported on an atomically flat substrate was found to efficiently transmit the properties of the substrate, but graphene suspended on a substrate with a rough nanoscale topography was completely opaque to the substrate properties. Our experimental observations revealed the morphology of the substrate to be a key factor affecting the transparency of graphene, and should be taken into account in order to optimally apply graphene as a protector of catalytic thin films and a booster of their catalysis.

Toluene diisocyanate exposure induces airway inflammation of bronchial epithelial cells via the activation of transient receptor potential melastatin 8.

Toluene diisocyanate (TDI) is the most important cause of occupational asthma (OA), and various pathogenic mechanisms have been suggested. Of these mechanisms, neurogenic inflammation is an important inducer of airway inflammation. Transient receptor potential melastatin 8 (TRPM8) is a well-established cold-sensing cation channel that is expressed in both neuronal cells and bronchial epithelial cells. A recent genome-wide association study of TDI-exposed workers found a significant association between the phenotype of TDI-induced OA and the single-nucleotide polymorphism rs10803666, which has been mapped to the TRPM8 gene. We hypothesized that TRPM8 located in airway epithelial cells may be involved in the pathogenic mechanisms of TDI-induced OA and investigated its role. Bronchial epithelial cells were treated with TDI in a dose- and time-dependent manner. The expression levels of TRPM8 mRNA and protein were determined by quantitative real-time polymerase chain reaction and western blotting. TDI-induced morphological changes in the cells were evaluated by immunocytochemistry. Alterations in the transcripts of inflammatory cytokines were examined in accordance with TRPM8 activation by TDI. TRPM8 expression at both the mRNA and protein levels was enhanced by TDI in airway epithelial cells. TRPM8 activation by TDI led to significant increases in the mRNA of interleukin (IL)-4, IL-13, IL-25 and IL-33. The increased expression of the cytokine genes by TDI was partly attenuated after treatment with a TRPM8 antagonist. TDI exposure induces increased expression of TRPM8 mRNA in airway epithelial cells coupled with enhanced expression of inflammatory cytokines, suggesting a novel role of TRPM8 in the pathogenesis of TDI-induced OA.

Randomized Phase III Study of Docetaxel Plus Cisplatin Versus Pemetrexed Plus Cisplatin as First-line Treatment of Nonsquamous Non-Small-cell Lung Cancer: A TRAIL Trial.

INTRODUCTION: To date, no prospective phase III trials have directly compared the efficacy of pemetrexed plus cisplatin (Pem-Cis) with docetaxel plus cisplatin (Doc-Cis) in patients with nonsquamous non-small-cell lung cancer. MATERIALS AND METHODS: A total of 148 chemotherapy-naive patients lacking driver mutations were randomized into 21-day regimens of cisplatin 70 mg/m2 with either docetaxel 60 mg/m2 (n = 71) or pemetrexed 500 mg/m2 (n = 77) for ≤ 4 cycles. The primary objective was to assess the noninferiority of progression-free survival (PFS) for patients receiving the Doc-Cis regimen. The secondary endpoints were the response rates, overall survival, and toxicity profiles. RESULTS: Partial remission was observed in 24 (31.2%) and 24 (33.8%) patients in the Pem-Cis and Doc-Cis groups, respectively. The median PFS was 4.7 months (95% confidence interval [CI], 4.4-5.0) in the Pem-Cis arm and 4.4 months (95% CI, 3.7-5.1) in the Doc-Cis arm (P > .05). The median overall survival was longer in the Doc-Cis arm (13.3 months; 95% CI, 8.1-18.5) than in the Pem-Cis arm (11.7 months; 95% CI, 8.6-14.8; P > .05). Between the 2 arms, no significant difference was found in the subsequent treatments after failure of first-line treatment. The rate of grade 3 or 4 neutropenia and febrile neutropenia was greater in the Doc-Cis arm than in the Pem-Cis arm. CONCLUSION: In nonsquamous non-small-cell lung cancer patients lacking driver mutations, the PFS and response rates were similar between the 2 arms, and toxicity was tolerable, although adverse events and more severe toxicities were observed more frequently in the Doc-Cis arm.

Lattice Transparency of Graphene.

Here, we demonstrated the transparency of graphene to the atomic arrangement of a substrate surface, i.e., the "lattice transparency" of graphene, by using hydrothermally grown ZnO nanorods as a model system. The growth behaviors of ZnO nanocrystals on graphene-coated and uncoated substrates with various crystal structures were investigated. The atomic arrangements of the nucleating ZnO nanocrystals exhibited a close match with those of the respective substrates despite the substrates being bound to the other side of the graphene. By using first-principles calculations based on density functional theory, we confirmed the energetic favorability of the nucleating phase following the atomic arrangement of the substrate even with the graphene layer present in between. In addition to transmitting information about the atomic lattice of the substrate, graphene also protected its surface. This dual role enabled the hydrothermal growth of ZnO nanorods on a Cu substrate, which otherwise dissolved in the reaction conditions when graphene was absent.

Selective nitrogen capture by porous hybrid materials containing accessible transition metal ion sites.

Selective dinitrogen binding to transition metal ions mainly covers two strategic domains: biological nitrogen fixation catalysed by metalloenzyme nitrogenases, and adsorptive purification of natural gas and air. Many transition metal-dinitrogen complexes have been envisaged for biomimetic nitrogen fixation to produce ammonia. Inspired by this concept, here we report mesoporous metal-organic framework materials containing accessible Cr(III) sites, able to thermodynamically capture N2 over CH4 and O2. This fundamental study integrating advanced experimental and computational tools confirmed that the separation mechanism for both N2/CH4 and N2/O2 gas mixtures is driven by the presence of these unsaturated Cr(III) sites that allows a much stronger binding of N2 over the two other gases. Besides the potential breakthrough in adsorption-based technologies, this proof of concept could open new horizons to address several challenges in chemistry, including the design of heterogeneous biomimetic catalysts through nitrogen fixation.