Cross-Species Comparative DNA Methylation Reveals Novel Insights into Complex Trait Genetics among Cattle, Sheep, and Goats.

The cross-species characterization of evolutionary changes in the functional genome can facilitate the translation of genetic findings across species and the interpretation of the evolutionary basis underlying complex phenotypes. Yet, this has not been fully explored between cattle, sheep, goats, and other mammals. Here, we systematically characterized the evolutionary dynamics of DNA methylation and gene expression in 3 somatic tissues (i.e. brain, liver, and skeletal muscle) and sperm across 7 mammalian species, including 3 ruminant livestock species (cattle, sheep, and goats), humans, pigs, mice, and dogs, by generating and integrating 160 DNA methylation and transcriptomic data sets. We demonstrate dynamic changes of DNA hypomethylated regions and hypermethylated regions in tissue-type manner across cattle, sheep, and goats. Specifically, based on the phylo-epigenetic model of DNA methylome, we identified a total of 25,074 hypomethylated region extension events specific to cattle, which participated in rewiring tissue-specific regulatory network. Furthermore, by integrating genome-wide association studies of 50 cattle traits, we provided novel insights into the genetic and evolutionary basis of complex phenotypes in cattle. Overall, our study provides a valuable resource for exploring the evolutionary dynamics of the functional genome and highlights the importance of cross-species characterization of multiomics data sets for the evolutionary interpretation of complex phenotypes in cattle livestock.

ETLD: an encoder-transformation layer-decoder architecture for protein contact and mutation effects prediction.

The latent features extracted from the multiple sequence alignments (MSAs) of homologous protein families are useful for identifying residue-residue contacts, predicting mutation effects, shaping protein evolution, etc. Over the past three decades, a growing body of supervised and unsupervised machine learning methods have been applied to this field, yielding fruitful results. Here, we propose a novel self-supervised model, called encoder-transformation layer-decoder (ETLD) architecture, capable of capturing protein sequence latent features directly from MSAs. Compared to the typical autoencoder model, ETLD introduces a transformation layer with the ability to learn inter-site couplings, which can be used to parse out the two-dimensional residue-residue contacts map after a simple mathematical derivation or an additional supervised neural network. ETLD retains the process of encoding and decoding sequences, and the predicted probabilities of amino acids at each site can be further used to construct the mutation landscapes for mutation effects prediction, outperforming advanced models such as GEMME, DeepSequence and EVmutation in general. Overall, ETLD is a highly interpretable unsupervised model with great potential for improvement and can be further combined with supervised methods for more extensive and accurate predictions.

AACFlow: an end-to-end model based on attention augmented convolutional neural network and flow-attention mechanism for identification of anticancer peptides.

MOTIVATION: Anticancer peptides (ACPs) have natural cationic properties and can act on the anionic cell membrane of cancer cells to kill cancer cells. Therefore, ACPs have become a potential anticancer drug with good research value and prospect. RESULTS: In this article, we propose AACFlow, an end-to-end model for identification of ACPs based on deep learning. End-to-end models have more room to automatically adjust according to the data, making the overall fit better and reducing error propagation. The combination of attention augmented convolutional neural network (AAConv) and multi-layer convolutional neural network (CNN) forms a deep representation learning module, which is used to obtain global and local information on the sequence. Based on the concept of flow network, multi-head flow-attention mechanism is introduced to mine the deep features of the sequence to improve the efficiency of the model. On the independent test dataset, the ACC, Sn, Sp, and AUC values of AACFlow are 83.9%, 83.0%, 84.8%, and 0.892, respectively, which are 4.9%, 1.5%, 8.0%, and 0.016 higher than those of the baseline model. The MCC value is 67.85%. In addition, we visualize the features extracted by each module to enhance the interpretability of the model. Various experiments show that our model is more competitive in predicting ACPs.

Targeted PLK1 suppression through RNA interference mediated by high-fidelity Cas13d mitigates osteosarcoma progression via TGF-ß/Smad3 signalling.

Osteosarcoma is the most common primary bone malignancy in children and adolescents. Overexpression of polo-like kinase 1 (PLK1) is frequent in osteosarcoma and drives disease progression and metastasis, making it a promising therapeutic target. In this study, we explored PLK1 knockdown in osteosarcoma cells using RNA interference mediated by high-fidelity Cas13d (hfCas13d). PLK1 was found to be significantly upregulated in osteosarcoma tumour tissues compared to normal bone. sgRNA-mediated PLK1 suppression via hfCas13d transfection inhibited osteosarcoma cell proliferation, induced G2/M cell cycle arrest, promoted apoptosis, reduced cell invasion and increased expression of the epithelial marker E-cadherin. Proximity labelling by TurboID coupled with co-immunoprecipitation identified novel PLK1 interactions with Smad3, a key intracellular transducer of TGF-ß signalling. PLK1 knockdown impaired Smad2/3 phosphorylation and modulated TGF-ß/Smad3 pathway inactivation. Finally, in vivo delivery of hfCas13d vectors targeting PLK1 substantially attenuated osteosarcoma xenograft growth in nude mice. Taken together, this study highlights PLK1 as a potential therapeutic target and driver of disease progression in osteosarcoma. It also demonstrates the utility of hfCas13d-mediated gene knockdown as a strategy for targeted therapy. Further optimization of PLK1 suppression approaches may ultimately improve clinical outcomes for osteosarcoma patients.

Design of Multifunctional Electrocatalysts for ORR/OER/HER/HOR: Janus Makes Difference.

Multifunctional electrocatalysts for hydrogen evolution reaction (HER), hydrogen oxidation reaction (HOR), oxygen evolution reaction (OER), and oxygen reduction reaction (ORR) have broad application prospects; However, realization of such kinds of materials remain difficulties because it requires the materials to have not only unique electronic properties, but multiple active centers to deal with different reactions. Here, employing density functional theory (DFT) computations, it is demonstrated that by decorating the Janus-type 2D transition metal dichalcogenide (TMD) of TaSSe with the single atoms, the materials can achieve multifunctionality to catalyze the ORR/OER/HER/HOR. Out of sixteen catalytic systems, Pt-VS (i.e., Pt atom embedded in the sulfur vacancy), Pd-VSe, and Pt-VSe@TaSSe are promising multifunctional catalysts with superior stability. Among them, the Pt-VS@TaSSe catalyst exhibits the highest activity with theoretical overpotentials ηORR = 0.40 V, ηOER = 0.39 V, and ηHER/HOR = 0.07 V, respectively, better than the traditional Pt (111), IrO2 (110). The interplays between the catalyst and the reaction intermediate over the course of the reaction are then systematically investigated. Generally, this study presents a viable approach for the design and development of advanced multifunctional electrocatalysts. It enriches the application of Janus, a new 2D material, in electrochemical energy storage and conversion technology.

R5hmCFDV: computational identification of RNA 5-hydroxymethylcytosine based on deep feature fusion and deep voting.

RNA 5-hydroxymethylcytosine (5hmC) is a kind of RNA modification, which is related to the life activities of many organisms. Studying its distribution is very important to reveal its biological function. Previously, high-throughput sequencing was used to identify 5hmC, but it is expensive and inefficient. Therefore, machine learning is used to identify 5hmC sites. Here, we design a model called R5hmCFDV, which is mainly divided into feature representation, feature fusion and classification. (i) Pseudo dinucleotide composition, dinucleotide binary profile and frequency, natural vector and physicochemical property are used to extract features from four aspects: nucleotide composition, coding, natural language and physical and chemical properties. (ii) To strengthen the relevance of features, we construct a novel feature fusion method. Firstly, the attention mechanism is employed to process four single features, stitch them together and feed them to the convolution layer. After that, the output data are processed by BiGRU and BiLSTM, respectively. Finally, the features of these two parts are fused by the multiply function. (iii) We design the deep voting algorithm for classification by imitating the soft voting mechanism in the Python package. The base classifiers contain deep neural network (DNN), convolutional neural network (CNN) and improved gated recurrent unit (GRU). And then using the principle of soft voting, the corresponding weights are assigned to the predicted probabilities of the three classifiers. The predicted probability values are multiplied by the corresponding weights and then summed to obtain the final prediction results. We use 10-fold cross-validation to evaluate the model, and the evaluation indicators are significantly improved. The prediction accuracy of the two datasets is as high as 95.41% and 93.50%, respectively. It demonstrates the stronger competitiveness and generalization performance of our model. In addition, all datasets and source codes can be found at https://github.com/HongyanShi026/R5hmCFDV.

Targeted Endoperoxides Delivering Singlet Oxygen to Cancer Cell Mitochondria: Exploration of the Therapeutic Potential.

The clinical practice of photodynamic therapy of cancer (PDT) is mostly limited to superficial types of skin cancer. The major reason behind this limited applicability is the need for light in the photogeneration of ROS, and in particular singlet oxygen. In order to circumvent this major roadblock, we designed and synthesized naphthalene-derived endoperoxides with mitochondria targeting triphenylphosphonium moieties. Here, we show that these compounds release singlet oxygen by thermal cycloreversion, and initiate cell death with IC50 < 10 µM in cancer cell cultures. The mouse 4T1 breast tumor model study, where the endoperoxide compound was introduced intraperitoneally, also showed highly promising results, with negligible systemic toxicity. Targeted delivery of singlet oxygen to cancer cell mitochondria could be the breakthrough needed to transform Photodynamic Therapy into a broadly applicable methodology for cancer treatment by keeping the central tenet and discarding problematic dependencies on oxygen or external light.

Effects of NEAT1 levels on cardiovascular events and prognosis in diabetic nephropathy patients undergoing peritoneal dialysis.

The purpose of this study was to provide observational indicators for clinically predicting cardiovascular events in patients with diabetic nephropathy (DN) undergoing peritoneal dialysis by determining the effects of nuclear enriched abundant transcript 1 (NEAT1) levels on the cardiovascular events and prognosis in DN patients receiving continuous ambulatory peritoneal dialysis (CAPD). A retrospective analysis was conducted on the data of 80 DN patients undergoing CAPD. Patients were assigned to NEAT1 high expression group and NEAT1 low expression group. NEAT1 had a substantially increased expression in the serum of DN patients, and it could serve as a potential biomarker for predicting the development of DN. Patients with highly expressed NEAT1 had an higher level of high-sensitivity C-reactive protein (hs-CRP), larger cardiac structural parameters left ventricular end-diastolic diameter (LVED), left ventricular end-systolic diameter (LVESD), interventricular septal diameter (IVSD) and left ventricular posterior wall diameter (LVPWD), but a notably lower cardiac function evaluation indicator left ventricular ejection fraction (LVEF) than those with lowly expressed NEAT1. The coefficient (r) of correlation between NEAT1 and hs-CRP level was 0.3585 (P=0.0011). The incidence rates of acute myocardial infarction, congestive heart failure and angina in NEAT1 high expression group were higher than those in NEAT1 low expression group. Patients with NEAT1 high expression exhibited a higher mortality rate than NEAT1 low expression group. With the increase in NEAT1 levels, the level of hs-CRP rose in DN patients undergoing CAPD. A higher expression level of NEAT1 indicates poorer cardiac function, higher incidence rates of cardiovascular adverse events and a poorer prognosis in diabetics undergoing CAPD.

First Report of Fruit Rot Disease on melon (Cucumis melo L.) Caused by Fusarium ipomoeae in China.

Fusarium rot on melon fruit has become an important postharvest disease for producers worldwide, typically involving multiple Fusarium pathogens (Khuna et al. 2022; Medeiros Araújo et al. 2021). In 2022, Fusarium fruit rot of muskmelon (Cucumis melo var. conomon) occurred sporadically in a field at Huainan Academy of Agricultural Sciences (32.658193º N, 117.064922º E) with an incidence of about 10%. Among these diseased muskmelons, a fruit exhibiting a white to yellowish colony athe intersection of the diseased and healthy tissues was collected and labeled TGGF22-17. The streak plate method was employed to isolate fungal spores on Bengal Red PDA (potato dextrose agar), which were then incubated at 25℃ in darkness. Following isolation and purification, a single-spore strain, TGGF22-17, was obtained and analyzed using morphological characters on PDA, synthetic nutrient agar (SNA) and carnation leaf agar (CLA) (Leslie and Summerell 2006), along with molecular identification. Colours were rated according to the color charts of Kornerup and Wanscher (1978). Based on the colony morphology on PDA, the isolate displayed a rosy buff or buff color with a white to buff margin. The colony margin was undulate, with the reverse transitioning from amber-yellow to honey-yellow. Aerial macroconidia on SNA were thin-walled, hyaline, mostly 3-5 septate, falcate, and measured 18.5-46.4 (x̄=34.2) × 2.9-4.8 (x̄ =3.9) µm in size (n =50). Sporodochial macroconidia on CLA were mostly five-septate with long apical and basal cells, exhibiting dorsiventral curvature. They were hyaline, with the apical cell hooked to tapering and the basal cell foot-shaped, measuring 46.5-89.6 (x̄ =72.3) × 3.5-5.0 (x̄ =4.3) µm in size (n = 100). Portions of three loci (TEF-1α, RPB1 and RPB2) were amplified and sequenced as described by Wang et al. (2019). Sequences were deposited in GenBank with accession number PP196583 to PP196585. The three gene sequences (TEF-1α, RPB1 and RPB2) of strain TGGF2022-17 shared 99.5% (629/632bp), 97.9% (1508/1540 bp) and 99.9% (1608/1609 bp) identity to the ex-type strain F. ipomoeae LC12165 respectively by pairwise DNA alignments on the FUSARIOID-ID database (https://www.fusarium.org). Phylogenetic analysis of the partial TEF-1α and RPB2 sequences with PhyloSuite (Zhang et al. 2020) showed the isolated fungus clustered with F. ipomoeae. Based on the morphological and phylogenetic analyses, TGGF22-17 was identified as F. ipomoeae. Pathogenicity tests were performed on healthy melons, which were surface-sterilized with 75% alcohol and wounded using a sterilized inoculation needle. A 4-mm diameter plug from a 7-day-old SNA culture of TGGF22-17 was aseptically inserted in the middle of the wound, sealed with plastic bag after absorbent cotton was included to maintain moisture. Five melons were each inoculated at three points. Noncolonized PDA agar plugs served as the negative control. The inoculated and uninoculated plugs were removed approximately 48 hours after inoculation. The melon inoculated with TGGF22-17 exhibited water-soaked black lesions 48h post-inoculation, resulting in a 100% infection rate (15/15). After 7 days, mycelium was obseved on the inoculated melons. No disease symptoms were observed on the uninoculated melons. To fulfill Koch's postulates, fungi were isolated from the inoculated fruit and confirmed as F. ipomoeae by morphological observation. Fusarium ipomoeae has been reported to cause fruit rot on winter squash (Cucurbita maxima) in Japan (Kitabayashi et al. 2023). To our knowledge, this is the first report of fruit rot on muskmelon caused by F. ipomoeae in China and this report will be valuable for monitoring and management of fruit rot disease on muskmelons.

Suppressing Nonradiative Recombination by Electron-Donating Substituents in 2D Conjugated Triphenylamine Polymers toward Efficient Perovskite Optoelectronics.

Highly efficient perovskite optoelectronics (POEs) have been limited by nonradiative recombination. We report a strategy to inhibit the nonradiative recombination of 2D triphenylamine polymers in the hole transport layer (HTL) via introducing electron-donating groups to enhance the conjugation effect and electron cloud density. The conjugated systems with electron-donating groups present smaller energy level oscillation compared to the ones with electron-absorbing groups, as confirmed by nonadiabatic molecular dynamics (NAMD) calculation. Further study reveals that the introduction of low-frequency phonons in the electron-donating group systems shortens the nonadiabatic coupling and inhibits the nonradiative recombination. Such electron-donating groups can decrease the valence band maximum of 2D polymers and promote hole transport. Our report provides a new design strategy to suppress nonradiative recombination in HTL for application in efficient POEs.

Co-Mutations and Possible Variation Tendency of the Spike RBD and Membrane Protein in SARS-CoV-2 by Machine Learning.

Since the onset of the coronavirus disease 2019 (COVID-19) pandemic, SARS-CoV-2 variants capable of breakthrough infections have attracted global attention. These variants have significant mutations in the receptor-binding domain (RBD) of the spike protein and the membrane (M) protein, which may imply an enhanced ability to evade immune responses. In this study, an examination of co-mutations within the spike RBD and their potential correlation with mutations in the M protein was conducted. The EVmutation method was utilized to analyze the distribution of the mutations to elucidate the relationship between the mutations in the spike RBD and the alterations in the M protein. Additionally, the Sequence-to-Sequence Transformer Model (S2STM) was employed to establish mapping between the amino acid sequences of the spike RBD and M proteins, offering a novel and efficient approach for streamlined sequence analysis and the exploration of their interrelationship. Certain mutations in the spike RBD, G339D-S373P-S375F and Q493R-Q498R-Y505, are associated with a heightened propensity for inducing mutations at specific sites within the M protein, especially sites 3 and 19/63. These results shed light on the concept of mutational synergy between the spike RBD and M proteins, illuminating a potential mechanism that could be driving the evolution of SARS-CoV-2.

The Inhibition Effect of Epigallocatechin-3-Gallate on the Co-Aggregation of Amyloid-ß and Human Islet Amyloid Polypeptide Revealed by Replica Exchange Molecular Dynamics Simulations.

Alzheimer's disease and Type 2 diabetes are two epidemiologically linked diseases which are closely associated with the misfolding and aggregation of amyloid proteins amyloid-ß (Aß) and human islet amyloid polypeptide (hIAPP), respectively. The co-aggregation of the two amyloid proteins is regarded as the fundamental molecular mechanism underlying their pathological association. The green tea extract epigallocatechin-3-gallate (EGCG) has been extensively demonstrated to inhibit the amyloid aggregation of Aß and hIAPP proteins. However, its potential role in amyloid co-aggregation has not been thoroughly investigated. In this study, we employed the enhanced-sampling replica exchange molecular dynamics simulation (REMD) method to investigate the effect of EGCG on the co-aggregation of Aß and hIAPP. We found that EGCG molecules substantially diminish the ß-sheet structures within the amyloid core regions of Aß and hIAPP in their co-aggregates. Through hydrogen-bond, π-π and cation-π interactions targeting polar and aromatic residues of Aß and hIAPP, EGCG effectively attenuates both inter-chain and intra-chain interactions within the co-aggregates. All these findings indicated that EGCG can effectively inhibit the co-aggregation of Aß and hIAPP. Our study expands the potential applications of EGCG as an anti-amyloidosis agent and provides therapeutic options for the pathological association of amyloid misfolding disorders.

Comprehensive analyses of 723 transcriptomes enhance genetic and biological interpretations for complex traits in cattle.

By uniformly analyzing 723 RNA-seq data from 91 tissues and cell types, we built a comprehensive gene atlas and studied tissue specificity of genes in cattle. We demonstrated that tissue-specific genes significantly reflected the tissue-relevant biology, showing distinct promoter methylation and evolution patterns (e.g., brain-specific genes evolve slowest, whereas testis-specific genes evolve fastest). Through integrative analyses of those tissue-specific genes with large-scale genome-wide association studies, we detected relevant tissues/cell types and candidate genes for 45 economically important traits in cattle, including blood/immune system (e.g., CCDC88C) for male fertility, brain (e.g., TRIM46 and RAB6A) for milk production, and multiple growth-related tissues (e.g., FGF6 and CCND2) for body conformation. We validated these findings by using epigenomic data across major somatic tissues and sperm. Collectively, our findings provided novel insights into the genetic and biological mechanisms underlying complex traits in cattle, and our transcriptome atlas can serve as a primary source for biological interpretation, functional validation, studies of adaptive evolution, and genomic improvement in livestock.

An improved residual network using deep fusion for identifying RNA 5-methylcytosine sites.

MOTIVATION: 5-Methylcytosine (m5C) is a crucial post-transcriptional modification. With the development of technology, it is widely found in various RNAs. Numerous studies have indicated that m5C plays an essential role in various activities of organisms, such as tRNA recognition, stabilization of RNA structure, RNA metabolism and so on. Traditional identification is costly and time-consuming by wet biological experiments. Therefore, computational models are commonly used to identify the m5C sites. Due to the vast computing advantages of deep learning, it is feasible to construct the predictive model through deep learning algorithms. RESULTS: In this study, we construct a model to identify m5C based on a deep fusion approach with an improved residual network. First, sequence features are extracted from the RNA sequences using Kmer, K-tuple nucleotide frequency component (KNFC), Pseudo dinucleotide composition (PseDNC) and Physical and chemical property (PCP). Kmer and KNFC extract information from a statistical point of view. PseDNC and PCP extract information from the physicochemical properties of RNA sequences. Then, two parts of information are fused with new features using bidirectional long- and short-term memory and attention mechanisms, respectively. Immediately after, the fused features are fed into the improved residual network for classification. Finally, 10-fold cross-validation and independent set testing are used to verify the credibility of the model. The results show that the accuracy reaches 91.87%, 95.55%, 92.27% and 95.60% on the training sets and independent test sets of Arabidopsis thaliana and M.musculus, respectively. This is a considerable improvement compared to previous studies and demonstrates the robust performance of our model. AVAILABILITY AND IMPLEMENTATION: The data and code related to the study are available at https://github.com/alivelxj/m5c-DFRESG.

DapNet-HLA: Adaptive dual-attention mechanism network based on deep learning to predict non-classical HLA binding sites.

Human leukocyte antigen (HLA) plays a vital role in immunomodulatory function. Studies have shown that immunotherapy based on non-classical HLA has essential applications in cancer, COVID-19, and allergic diseases. However, there are few deep learning methods to predict non-classical HLA alleles. In this work, an adaptive dual-attention network named DapNet-HLA is established based on existing datasets. Firstly, amino acid sequences are transformed into digital vectors by looking up the table. To overcome the feature sparsity problem caused by unique one-hot encoding, the fused word embedding method is used to map each amino acid to a low-dimensional word vector optimized with the training of the classifier. Then, we use the GCB (group convolution block), SENet attention (squeeze-and-excitation networks), BiLSTM (bidirectional long short-term memory network), and Bahdanau attention mechanism to construct the classifier. The use of SENet can make the weight of the effective feature map high, so that the model can be trained to achieve better results. Attention mechanism is an Encoder-Decoder model used to improve the effectiveness of RNN, LSTM or GRU (gated recurrent neural network). The ablation experiment shows that DapNet-HLA has the best adaptability for five datasets. On the five test datasets, the ACC index and MCC index of DapNet-HLA are 4.89% and 0.0933 higher than the comparison method, respectively. According to the ROC curve and PR curve verified by the 5-fold cross-validation, the AUC value of each fold has a slight fluctuation, which proves the robustness of the DapNet-HLA. The codes and datasets are accessible at https://github.com/JYY625/DapNet-HLA.

Hybrid mixed-dimensional WTe2/CsPbI3perovskite heterojunction for high-performance photodetectors.

Perovskites have showed significant potential for the application in photodetectors due to their outstanding electrical and optical properties. Integrating two-dimensional (2D) materials with perovskites can make full use of the high carrier mobility of 2D materials and strong light absorption of perovskite to realize excellent optoelectrical properties. Here, we demonstrate a photodetector based on the WTe2/CsPbI3heterostructure. The quenching and the shortened lifetime of photoluminescence (PL) for CsPbI3perovskite confirms the efficient charge transfer at the WTe2/CsPbI3heterojunction. After coupled with WTe2, the photoresponsivity of the CsPbI3photodetector is improved by almost two orders of magnitude due to the high-gain photogating effect. The WTe2/CsPbI3heterojunction photodetector reveals a large responsivity of 1157 A W-1and a high detectivity of 2.1 × 1013Jones. The results pave the way for the development of high-performance optoelectronic devices based on 2D materials/perovskite heterojunctions.

Machine learning and density functional theory simulation of the electronic structural properties for novel quaternary semiconductors.

In order to accelerate the application of quaternary optoelectronic materials in the field of luminescence, it is crucial to develop new quaternary semiconductor materials with excellent properties. However, faced with vast alternative quaternary semiconductors, traditional trial-and-error methods tend to be laborious and inefficient. Here, we combined machine learning (ML) with density functional theory (DFT) calculation to predict the bandgaps of 2180 quaternary semiconductors, most of which were undeveloped but environmentally friendly. The evaluation coefficient (R2) of the model using a random forest algorithm was up to 0.93 in ML. Four novel quaternary semiconductors with direct bandgaps: Ag2InGaS4, AgZn2InS4, Ag2ZnSnS4, and AgZn2GaS4, were selected from the ML model. Then their electronic structures and optical properties were further verified and studied by DFT calculations, which demonstrated that the four quaternary semiconductors had direct bandgaps, a small effective mass, and a large exciton binding energy and Stokes shift. Our calculation could significantly speed up the discovery of novel optoelectronic semiconductors and has a certain reference value for the study of luminescent materials and devices.

Adsorption and sensing performance of air pollutants on a ß-TeO2 monolayer: a first-principles study.

Two-dimensional (2D) ß-TeO2 is a novel semiconductor with potential applications in electronic circuits due to its air-stability and ultra-high carrier mobility. In this study, we explore the possibility of using a 2D ß-TeO2 monolayer for the detection of gaseous pollutants including SO2, NO2, H2S, CO2, CO, and NH3 gas molecules based on first-principles calculations. The adsorption properties including the adsorption energy, adsorption distance and charge transfer indicate that the interaction between 2D ß-TeO2 and the six gases is via a physisorption mechanism. Among the six gas adsorption systems, the SO2 adsorption system has the most negative adsorption energy and the largest charge transfer. In addition, the adsorption of SO2 obviously changes the electrical conductivity of the ß-TeO2 monolayer because the band gap decreases from 2.727 eV to 1.897 eV after adsorbing SO2. Our results suggest that the 2D ß-TeO2 should be an eminently promising SO2 sensing material.

Transition from Schottky to ohmic contacts in the C31 and MoS2 van der Waals heterostructure.

The utilization of conventional metal contacts has restricted the industrial implementation of two-dimensional channel materials. To address this issue, we conducted first-principles calculations to investigate the interface properties of C31 and MoS2 contacts. An ohmic contact and a low van der Waals barrier were found in the C31/MoS2 heterostructure. Our findings provide a promising new contact metal material for two-dimensional nanodevices based on MoS2.

Graphene/C2N lateral heterostructures as promising anode materials for lithium-ion batteries.

Two-dimensional (2D) heterostructures have been proposed as potential anode materials for lithium-ion batteries due to their large surface areas and excellent electronic properties. In this study, we employ first-principles calculations to investigate the structural stability, electronic properties, and ion diffusion behaviors of 2D graphene/C2N lateral heterostructures. Three species of (5, 26), (11, 26), and (17, 26) heterostructures are chosen to explore the effects of graphene components on electronic properties. The results show that graphene/C2N lateral heterostructures exhibit good dynamic stability due to a small lattice mismatch and strong chemical interaction at the heterojunction interface. By introducing zero-gap graphene, these heterostructures acquire good electronic conductivity with small direct band gaps. The component ratio of graphene can significantly tune the band gap, showing a monotonic decrease as the ratio increases. Moreover, the introduction of C2N components can greatly improve the lithium capacities of heterostructures. Small diffusion energy barriers (0.257-0.273 eV) and a low average operating voltage of 0.758 V are observed in graphene/C2N heterostructures. The effects of graphene components and valence states on Li migration are discussed. Our results demonstrate that the graphene/C2N lateral heterostructure can effectively combine the advantages of graphene and the C2N monolayer, showing great promise as an anode material for lithium-ion batteries.