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Abstract Typical ductile materials are metals, which deform by the motion of defects like dislocations in association with non-directional metallic bonds. Unfortunately, this textbook mechanism does not operate in most inorganic semiconductors at ambient temperature, thus severely limiting the development of much-needed flexible electronic devices. We found a shear-deformation mechanism in a recently discovered ductile semiconductor, monoclinic-silver sulfide (Ag2S), which is defect-free, omni-directional, and preserving perfect crystallinity. Our first-principles molecular dynamics simulations elucidate the ductile deformation mechanism in monoclinic-Ag2S under six types of shear systems. Planer mass movement of sulfur atoms plays an important role for the remarkable structural recovery of sulfur-sublattice. This in turn arises from a distinctively high symmetry of the anion-sublattice in Ag2S, which is not seen in other brittle silver chalcogenides. Such mechanistic and lattice-symmetric understanding provides a guideline for designing even higher-performance ductile inorganic semiconductors.

Hetero-structures made from vertically stacked monolayers of transition metal dichalcogenides hold great potential for optoelectronic and thermoelectric devices. Discovery of the optimal layered material for specific applications necessitates the estimation of key material properties, such as electronic band structure and thermal transport coefficients. However, screening of material properties via brute force ab initio calculations of the entire material structure space exceeds the limits of current computing resources. Moreover, the functional dependence of material properties on the structures is often complicated, making simplistic statistical procedures for prediction difficult to employ without large amounts of data collection. Here, we present a Gaussian process regression model, which predicts material properties of an input hetero-structure, as well as an active learning model based on Bayesian optimization, which can efficiently discover the optimal hetero-structure using a minimal number of ab initio calculations. The electronic band gap, conduction/valence band dispersions, and thermoelectric performance are used as representative material properties for prediction and optimization. The Materials Project platform is used for electronic structure computation, while the BoltzTraP code is used to compute thermoelectric properties. Bayesian optimization is shown to significantly reduce the computational cost of discovering the optimal structure when compared with finding an optimal structure by building a regression model to predict material properties. The models can be used for predictions with respect to any material property and our software, including data preparation code based on the Python Materials Genomics (PyMatGen) library as well as python-based machine learning code, is available open source.

A wide variety of two-dimensional layered materials are synthesized by liquid-phase exfoliation. Here we examine exfoliation of MoS2 into nanosheets in a mixture of water and isopropanol (IPA) containing cavitation bubbles. Using force fields optimized with experimental data on interfacial energies between MoS2 and the solvent, multimillion-atom molecular dynamics simulations are performed in conjunction with experiments to examine shock-induced collapse of cavitation bubbles and the resulting exfoliation of MoS2. The collapse of cavitation bubbles generates high-speed nanojets and shock waves in the solvent. Large shear stresses due to the nanojet impact on MoS2 surfaces initiate exfoliation, and shock waves reflected from MoS2 surfaces enhance exfoliation. Structural correlations in the solvent indicate that shock induces an ice VII like motif in the first solvation shell of water.

Abstract Predictive materials synthesis is the primary bottleneck in realizing functional and quantum materials. Strategies for synthesis of promising materials are currently identified by time-consuming trial and error and there are no known predictive schemes to design synthesis parameters for materials. We use offline reinforcement learning (RL) to predict optimal synthesis schedules, i.e., a time-sequence of reaction conditions like temperatures and concentrations, for the synthesis of semiconducting monolayer MoS2 using chemical vapor deposition. The RL agent, trained on 10,000 computational synthesis simulations, learned threshold temperatures and chemical potentials for onset of chemical reactions and predicted previously unknown synthesis schedules that produce well-sulfidized crystalline, phase-pure MoS2. The model can be extended to multi-task objectives such as predicting profiles for synthesis of complex structures including multi-phase heterostructures and can predict long-time behavior of reacting systems, far beyond the domain of molecular dynamics simulations, making these predictions directly relevant to experimental synthesis.

Abstract Mechanical behavior of 2D materials such as MoS2 can be tuned by the ancient art of kirigami. Experiments and atomistic simulations show that 2D materials can be stretched more than 50% by strategic insertion of cuts. However, designing kirigami structures with desired mechanical properties is highly sensitive to the pattern and location of kirigami cuts. We use reinforcement learning (RL) to generate a wide range of highly stretchable MoS2 kirigami structures. The RL agent is trained by a small fraction (1.45%) of molecular dynamics simulation data, randomly sampled from a search space of over 4 million candidates for MoS2 kirigami structures with 6 cuts. After training, the RL agent not only proposes 6-cut kirigami structures that have stretchability above 45%, but also gains mechanistic insight to propose highly stretchable (above 40%) kirigami structures consisting of 8 and 10 cuts from a search space of billion candidates as zero-shot predictions.

Photo-induced non-radiative energy dissipation is a potential pathway to induce structural-phase transitions in two-dimensional materials. For advancing this field, a quantitative understanding of real-time atomic motion and lattice temperature is required. However, this understanding has been incomplete due to a lack of suitable experimental techniques. Here, we use ultrafast electron diffraction to directly probe the subpicosecond conversion of photoenergy to lattice vibrations in a model bilayered semiconductor, molybdenum diselenide. We find that when creating a high charge carrier density, the energy is efficiently transferred to the lattice within one picosecond. First-principles nonadiabatic quantum molecular dynamics simulations reproduce the observed ultrafast increase in lattice temperature and the corresponding conversion of photoenergy to lattice vibrations. Nonadiabatic quantum simulations further suggest that a softening of vibrational modes in the excited state is involved in efficient and rapid energy transfer between the electronic system and the lattice.

Recent discoveries of polar topological structures (e.g., skyrmions and merons) in ferroelectric/paraelectric heterostructures have opened a new field of polar topotronics. However, how complex interplay of photoexcitation, electric field and mechanical strain controls these topological structures remains elusive. To address this challenge, we have developed a computational approach at the nexus of machine learning and first-principles simulations. Our multiscale neural-network quantum molecular dynamics molecular mechanics approach achieves orders-of-magnitude faster computation, while maintaining quantum-mechanical accuracy for atoms within the region of interest. This approach has enabled us to investigate the dynamics of vortex states formed in PbTiO3 nanowires embedded in SrTiO3. We find topological switching of these vortex states to topologically trivial, uniformly polarized states using electric field and trivial domain-wall states using shear strain. These results, along with our earlier results on optical control of polar topology, suggest an exciting new avenue toward opto-electro-mechanical control of ultrafast, ultralow-power polar topotronic devices.

Background: Chronic Gulf War Illness (GWI) is characterized by cognitive and mood impairments, as well as persistent neuroinflammation and oxidative stress. This study aimed to investigate the efficacy of Epidiolex®, a Food and Drug Administration (FDA)-approved cannabidiol (CBD), in improving brain function in a rat model of chronic GWI. Methods: Six months after exposure to low doses of GWI-related chemicals [pyridostigmine bromide, N,N-diethyl-meta-toluamide (DEET), and permethrin (PER)] along with moderate stress, rats with chronic GWI were administered either vehicle (VEH) or CBD (20 mg/kg, oral) for 16 weeks. Neurobehavioral tests were conducted on 11 weeks after treatment initiation to evaluate the performance of rats in tasks related to associative recognition memory, object location memory, pattern separation, and sucrose preference. The effect of CBD on hyperalgesia was also examined. The brain tissues were processed for immunohistochemical and molecular studies following behavioral tests. Results: GWI rats treated with VEH exhibited impairments in all cognitive tasks and anhedonia, whereas CBD-treated GWI rats showed improvements in all cognitive tasks and no anhedonia. Additionally, CBD treatment alleviated hyperalgesia in GWI rats. Analysis of hippocampal tissues from VEH-treated rats revealed astrocyte hypertrophy and increased percentages of activated microglia presenting NOD-, LRR- and pyrin domain-containing protein 3 (NLRP3) complexes as well as elevated levels of proteins involved in NLRP3 inflammasome activation and Janus kinase/signal transducers and activators of the transcription (JAK/STAT) signaling. Furthermore, there were increased concentrations of proinflammatory and oxidative stress markers along with decreased neurogenesis. In contrast, the hippocampus from CBD-treated GWI rats displayed reduced levels of proteins mediating the activation of NLRP3 inflammasomes and JAK/STAT signaling, normalized concentrations of proinflammatory cytokines and oxidative stress markers, and improved neurogenesis. Notably, CBD treatment did not alter the concentration of endogenous cannabinoid anandamide in the hippocampus. Conclusions: The use of an FDA-approved CBD (Epidiolex®) has been shown to effectively alleviate cognitive and mood impairments as well as hyperalgesia associated with chronic GWI. Importantly, the improvements observed in rats with chronic GWI in this study were attributed to the ability of CBD to significantly suppress signaling pathways that perpetuate chronic neuroinflammation. [ABSTRACT FROM AUTHOR]

The global rise in human population has escalated food demand, leading the agricultural sector to heavily rely on pesticides, which over time contaminate soil. To preserve the environment, it is imperative to adopt sustainable agricultural practices. Pesticides adversely affect earthworm survival and cocoon production, rendering them unsuitable for agricultural purposes. Bioremediation combined with bioaugmentation offers a solution to improve pesticide-contaminated agricultural soil. Although earthworms are effective in soil bioremediation, even sublethal doses of pesticides can diminish their lifespan and reproductive capabilities. This study specifically highlights the impact of sublethal toxicity from AlP (Aluminium phosphide) and DDVP (dichlorvos) on Eisenia fetida. Initial findings indicate a substantial reduction in cocoon production in the early stages following pesticide exposure, yet subsequent bioaugmentation with cow dung marginally increased cocoon production. Thus, incorporating bioaugmentation with cow dung is essential when employing earthworms for bioremediation in pesticide-contaminated soil. Furthermore, this study underscores the significant harm that pesticide residues and sublethal doses inflict on soil fauna and the microorganisms residing within macrofauna. [ABSTRACT FROM AUTHOR]

Abstract Engineering thermal transport in two dimensional materials, alloys and heterostructures is critical for the design of next-generation flexible optoelectronic and energy harvesting devices. Direct experimental characterization of lattice thermal conductivity in these ultra-thin systems is challenging and the impact of dopant atoms and hetero-phase interfaces, introduced unintentionally during synthesis or as part of deliberate material design, on thermal transport properties is not understood. Here, we use non-equilibrium molecular dynamics simulations to calculate lattice thermal conductivity of $${\mathrm {(Mo|W)Se_2}}$$ ( Mo | W ) Se 2 monolayer crystals including $${\mathrm {Mo}}_{1-x}{\mathrm {W}}_x{\mathrm {Se_2}}$$ Mo 1 - x W x Se 2 alloys with substitutional point defects, periodic $${\mathrm {MoSe_2}|\mathrm {WSe_2}}$$ MoSe 2 | WSe 2 heterostructures with characteristic length scales and scale-free fractal $${\mathrm {MoSe_2}}|{\mathrm {WSe_2}}$$ MoSe 2 | WSe 2 heterostructures. Each of these features has a distinct effect on phonon propagation in the crystal, which can be used to design fractal and periodic alloy structures with highly tunable thermal conductivities. This control over lattice thermal conductivity will enable applications ranging from thermal barriers to thermoelectrics.

Neutrophils play a significant role in determining disease severity following SARS-CoV-2 infection. Gene and protein expression defines several neutrophil clusters in COVID-19, including the emergence of low density neutrophils (LDN) that are associated with severe disease. The functional capabilities of these neutrophil clusters and correlation with gene and protein expression are unknown. To define host defense and immunosuppressive functions of normal density neutrophils (NDN) and LDN from COVID-19 patients, we recruited 64 patients with severe COVID-19 and 26 healthy donors (HD). Phagocytosis, respiratory burst activity, degranulation, neutrophil extracellular trap (NET) formation, and T-cell suppression in those neutrophil subsets were measured. NDN from severe/critical COVID-19 patients showed evidence of priming with enhanced phagocytosis, respiratory burst activity, and degranulation of secretory vesicles and gelatinase and specific granules, while NET formation was similar to HD NDN. COVID LDN response was impaired except for enhanced NET formation. A subset of COVID LDN with intermediate CD16 expression (CD16Int LDN) promoted T cell proliferation to a level similar to HD NDN, while COVID NDN and the CD16Hi LDN failed to stimulate T-cell activation. All 3 COVID-19 neutrophil populations suppressed stimulation of IFN-γ production, compared to HD NDN. We conclude that NDN and LDN from COVID-19 patients possess complementary functional capabilities that may act cooperatively to determine disease severity. We predict that global neutrophil responses that induce COVID-19 ARDS will vary depending on the proportion of neutrophil subsets.

The Energy Sciences Network (ESnet) is the primary provider of network connectivity for the US De- partment of Energy (DOE) Office of Science (SC), the single largest supporter of basic research in the physical sciences in the United States. In support of the Office of Science programs, ESnet regularly updates and refreshes its understanding of the networking requirements of the instruments, facilities, scientists, and science programs that it serves. This focus has helped ESnet to be a highly successful enabler of scientific discovery for over 25 years.In September 2014, ESnet and the Office of Basic Energy Sciences (BES), of the DOE Office of Science, organized a review to characterize the networking requirements of the programs funded by the BES program office.Several key findings resulted from the review.

Summary: Atomically thin two-dimensional (2D) transition metal dichalcogenides (TMDCs) have attracted significant attention owing to their prosperity in material research. The inimitable features of TMDCs triggered the emerging applications in diverse areas. In this review, we focus on the tailored and engineering of the crystal lattice of TMDCs that finally enhance the efficiency of the material properties. We highlight several preparation techniques and recent advancements in compositional engineering of TMDCs structure. We summarize different approaches for TMDCs such as doping and alloying with different materials, alloying with other 2D metals, and scrutinize the technological potential of these methods. Beyond that, we also highlight the recent significant advancement in preparing 2D quasicrystals and alloying the 2D TMDCs with MAX phases. Finally, we highlight the future perspectives for crystal engineering in TMDC materials for structure stability, machine learning concept marge with materials, and their emerging applications.

Dengue is a serious public health concern worldwide, with ∼3 billion people at risk of contracting dengue virus (DENV) infections, with some suffering severe consequences of disease and leading to death. Currently, there is no broad use vaccine or drug available for the prevention or treatment of dengue, which leaves only anti-mosquito strategies to combat the dengue menace. The present study is an extension of our earlier study aimed at determining the in vitro and in vivo protective effects of a plant-derived phytopharmaceutical drug for the treatment of dengue. In our previous report, we had identified a methanolic extract of aerial parts of Cissampelos pareira to exhibit in vitro and in vivo anti-dengue activity against all the four DENV serotypes. The dried aerial parts of C. pareira supplied by local vendors were often found to be mixed with aerial parts of another plant of the same Menispermaceae family, Cocculus hirsutus, which shares common homology with C. pareira. In the current study, we have found C. hirsutus to have more potent anti-dengue activity as compared with C. pareira. The stem part of C. hirsutus was found to be more potent (∼25 times) than the aerial part (stem and leaf) irrespective of the extraction solvent used, viz., denatured spirit, hydro-alcohol (50:50), and aqueous. Moreover, the anti-dengue activity of stem extract in all the solvents was comparable. Hence, an aqueous extract of the stem of C. hirsutus (AQCH) was selected due to greater regulatory compliance. Five chemical markers, viz., Sinococuline, 20-Hydroxyecdysone, Makisterone-A, Magnoflorine, and Coniferyl alcohol, were identified in fingerprinting analysis. In a test of primary dengue infection in the AG129 mice model, AQCH extract at 25 mg/kg body weight exhibited protection when administered four and three times a day. The AQCH was also protective in the secondary DENV-infected AG129 mice model at 25 mg/kg/dose when administered four and three times a day. Additionally, the AQCH extract reduced serum viremia and small intestinal pathologies, viz., viral load, pro-inflammatory cytokines, and vascular leakage. Based on these findings, we have undertaken the potential preclinical development of C. hirsutus-based phytopharmaceutical, which could be studied further for its clinical development for treating dengue.