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.

High-Performance Sub-10 nm Two-Dimensional SbSeBr Transistors through Transport Orientation.

Exploring two-dimensional (2D) materials with a small carrier effective mass and suitable band gap is crucial for the design of metal oxide semiconductor field effect transistors (MOSFETs). Here, the quantum transport properties of stable 2D SbSeBr are simulated on the basis of first-principles calculations. Monolayer SbSeBr proves to be a competitive channel material, offering a suitable band gap of 1.18 eV and a small electron effective mass (me*) of 0.22m0. The 2D SbSeBr field effect transistor (FET) with 8 nm channel length exhibits a high on-state current of 1869 µA/µm, low power consumption of 0.080 fJ/µm, and small delay time of 0.062 ps, which can satisfy the requirements of the International Technology Roadmap for Semiconductors for high-performance devices. Moreover, despite the monolayer SbSeBr having an isotropic me*, the asymmetrical band trends enable SbSeBr FETs to display transport orientation, which emphasizes the importance of band trends and provides valuable insights for selecting channel materials.

Four Parallel Pathways in T4 Ligase-Catalyzed Repair of Nicked DNA with Diverse Bending Angles.

The structural diversity of biological macromolecules in different environments contributes complexity to enzymological processes vital for cellular functions. Fluorescence resonance energy transfer and electron microscopy are used to investigate the enzymatic reaction of T4 DNA ligase catalyzing the ligation of nicked DNA. The data show that both the ligase-AMP complex and the ligase-AMP-DNA complex can have four conformations. This finding suggests the parallel occurrence of four ligation reaction pathways, each characterized by specific conformations of the ligase-AMP complex that persist in the ligase-AMP-DNA complex. Notably, these complexes have DNA bending angles of ≈0°, 20°, 60°, or 100°. The mechanism of parallel reactions challenges the conventional notion of simple sequential reaction steps occurring among multiple conformations. The results provide insights into the dynamic conformational changes and the versatile attributes of T4 DNA ligase and suggest that the parallel multiple reaction pathways may correspond to diverse T4 DNA ligase functions. This mechanism may potentially have evolved as an adaptive strategy across evolutionary history to navigate complex environments.

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.

iACVP-MR: Accurate Identification of Anti-coronavirus Peptide based on Multiple Features Information and Recurrent Neural Network.

BACKGROUND: Over the years, viruses have caused human illness and threatened human health. Therefore, it is pressing to develop anti-coronavirus infection drugs with clear function, low cost, and high safety. Anti-coronavirus peptide (ACVP) is a key therapeutic agent against coronavirus. Traditional methods for finding ACVP need a great deal of money and man power. Hence, it is a significant task to establish intelligent computational tools to able rapid, efficient and accurate identification of ACVP. METHODS: In this paper, we construct an excellent model named iACVP-MR to identify ACVP based on multiple features and recurrent neural networks. Multiple features are extracted by using reduced amino acid component and dipeptide component, compositions of k-spaced amino acid pairs, BLOSUM62 encoder according to the N5C5 sequence, as well as second-order moving average approach based on 16 physicochemical properties. Then, two recurrent neural networks named long-short term memory (LSTM) and bidirectional gated recurrent unit (BiGRU) combined attention mechanism are used for feature fusion and classification, respectively. RESULTS: The accuracies of ENNAVIA-C and ENNAVIA-D datasets under the 10-fold cross-validation are 99.15% and 98.92%, respectively, and other evaluation indexes have also obtained satisfactory results. The experimental results show that our model is superior to other existing models. CONCLUSION: The iACVP-MR model can be viewed as a powerful and intelligent tool for the accurate identification of ACVP. The datasets and source codes for iACVP-MR are freely downloaded at https://github.com/yunyunliang88/iACVP-MR.

EACVP: An ESM-2 LM Framework Combined CNN and CBAM Attention to Predict Anti-coronavirus Peptides.

BACKGROUND: The novel coronavirus pneumonia (COVID-19) outbreak in late 2019 killed millions worldwide. Coronaviruses cause diseases such as severe acute respiratory syndrome (SARS-Cov) and SARS-COV-2. Many peptides in the host defense system have antiviral activity. How to establish a set of efficient models to identify anti-coronavirus peptides is a meaningful study. METHODS: Given this, a new prediction model EACVP is proposed. This model uses the evolutionary scale language model (ESM-2 LM) to characterize peptide sequence information. The ESM model is a natural language processing model trained by machine learning technology. It is trained on a highly diverse and dense dataset (UR50/D 2021_04) and uses the pre-trained language model to obtain peptide sequence features with 320 dimensions. Compared with traditional feature extraction methods, the information represented by ESM-2 LM is more comprehensive and stable. Then, the features are input into the convolutional neural network (CNN), and the convolutional block attention module (CBAM) lightweight attention module is used to perform attention operations on CNN in space dimension and channel dimension. To verify the rationality of the model structure, we performed ablation experiments on the benchmark and independent test datasets. We compared the EACVP with existing methods on the independent test dataset. RESULTS: Experimental results show that ACC, F1-score, and MCC are 3.95%, 35.65% and 0.0725 higher than the most advanced methods, respectively. At the same time, we tested EACVP on ENNAVIA-C and ENNAVIA-D data sets, and the results showed that EACVP has good migration and is a powerful tool for predicting anti-coronavirus peptides. CONCLUSION: The results prove that this model EACVP could fully characterize the peptide information and achieve high prediction accuracy. It can be generalized to different data sets. The data and code of the article have been uploaded to https://github.- com/JYY625/EACVP.git.

Identifying atomically thin isolated-band channels for intrinsic steep-slope transistors by high-throughput study.

Developing low-power FETs holds significant importance in advancing logic circuits, especially as the feature size of MOSFETs approaches sub-10 nanometers. However, this has been restricted by the thermionic limitation of SS, which is limited to 60 mV per decade at room temperature. Herein, we proposed a strategy that utilizes 2D semiconductors with an isolated-band feature as channels to realize sub-thermionic SS in MOSFETs. Through high-throughput calculations, we established a guiding principle that combines the atomic structure and orbital interaction to identify their sub-thermionic transport potential. This guides us to screen 192 candidates from the 2D material database comprising 1608 systems. Additionally, the physical relationship between the sub-thermionic transport performances and electronic structures is further revealed, which enables us to predict 15 systems with promising device performances for low-power applications with supply voltage below 0.5 V. This work opens a new way for the low-power electronics based on 2D materials and would inspire extensive interests in the experimental exploration of intrinsic steep-slope MOSFETs.

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.

Progress of charge carrier dynamics and regulation strategies in 2D CxNy-based heterojunctions.

Two-dimensional carbon nitrides (CxNy) have gained significant attention in various fields including hydrogen energy development, environmental remediation, optoelectronic devices, and energy storage owing to their extensive surface area, abundant raw materials, high chemical stability, and distinctive physical and chemical characteristics. One effective approach to address the challenges of limited visible light utilization and elevated carrier recombination rates is to establish heterojunctions for CxNy-based single materials (e.g. C2N3, g-C3N4, C3N4, C4N3, C2N, and C3N). The carrier generation, migration, and recombination of heterojunctions with different band alignments have been analyzed starting from the application of CxNy with metal oxides, transition metal sulfides (selenides), conductive carbon, and Cx'Ny' heterojunctions. Additionally, we have explored diverse strategies to enhance heterojunction performance from the perspective of carrier dynamics. In conclusion, we present some overarching observations and insights into the challenges and opportunities associated with the development of advanced CxNy-based heterojunctions.

Molecular Insights into the Inhibition and Disaggregation Effects of EGCG on Aß40 and Aß42 Cofibrillation.

The misfolding and aggregation of amyloid-ß (Aß) peptides play a pivotal role in the pathogenesis of Alzheimer's disease (AD). Aß40 and Aß42, the two primary isoforms of Aß, can not only self-aggregate into homogeneous aggregates but also coaggregate to form mixed fibrils. Epigallocatechin-3-gallate (EGCG), a prominent tea polyphenol, has shown the capability to prevent the self-aggregation of Aß40 and Aß42 peptides and disaggregate their homogeneous fibrils. However, its effects on the cofibrillation of Aß40 and Aß42 have not yet been explored. Here, we employed molecular dynamic simulations to investigate the effects of EGCG on the coaggregation of Aß40 and Aß42, as well as on their mixed fibril. Our findings indicated that EGCG effectively inhibits the codimerization of Aß40 and Aß42 primarily by impeding the interchain interaction between the two isoforms. The key binding sites for EGCG on Aß40 and Aß42 are the polar residues and aromatic residues, engaging in hydrogen-bond , π-π, and cation-π interactions with EGCG. Additionally, EGCG disaggregates the Aß40-Aß42 mixed fibril by reducing its long-range interaction through similar binding sites and interactions as those between EGCG and Aß40-Aß42 heterodimers. Our research reveals the comprehensive inhibition and disaggregation effects of EGCG on the cofibrillation of Aß isoforms, which provides further support for the development of EGCG as an effective antiaggregation agent for AD.

Multi-scale characterization and analysis of cellular viscoelastic mechanical phenotypes by atomic force microscopy.

The viscoelasticity of cells serves as a biomarker that reveals changes induced by malignant transformation, which aids the cytological examinations. However, differences in the measurement methods and parameters have prevented the consistent and effective characterization of the viscoelastic phenotype of cells. To address this issue, nanomechanical indentation experiments were conducted using an atomic force microscope (AFM). Multiple indentation methods were applied, and the indentation parameters were gradually varied to measure the viscoelasticity of normal liver cells and cancerous liver cells to create a database. This database was employed to train machine-learning algorithms in order to analyze the differences in the viscoelasticity of different types of cells and as well as to identify the optimal measurement methods and parameters. These findings indicated that the measurement speed significantly influenced viscoelasticity and that the classification difference between the two cell types was most evident at 5 µm/s. In addition, the precision and the area under the receiver operating characteristic curve were comparatively analyzed for various widely employed machine-learning algorithms. Unlike previous studies, this research validated the effectiveness of measurement parameters and methods with the assistance of machine-learning algorithms. Furthermore, the results confirmed that the viscoelasticity obtained from the multiparameter indentation measurement could be effectively used for cell classification. RESEARCH HIGHLIGHTS: This study aimed to analyze the viscoelasticity of liver cancer cells and liver cells. Different nano-indentation methods and parameters were used to measure the viscoelasticity of the two kinds of cells. The neural network algorithm was used to reverse analyze the dataset, and the methods and parameters for accurate classification and identification of cells are successfully found.

Longitudinal Distribution Map of the Active Components and Endophytic Fungi in Angelica sinensis (Oliv.) Diels Root and Their Potential Correlations.

The three distinct medicinal parts of Angelica sinensis (Oliv.) Diels (Ang) roots are the head, body, and tail (ARH, ARB, and ART, respectively). How endophytic fungi shape the differences in metabolic components among these parts remains unclear. We quantified the distribution of active components and endophytic fungi along the ARH, ARB, and ART and their relationships. Based on the metabolic components and their abundances detected via non-target metabolism, the different medicinal parts were distinguishable. The largest number of dominant metabolic components was present in ART. The difference between ART and ARH was the greatest, and ARB was in a transitional state. The dominant active molecules in ART highlight their effects in haemodynamics improvement, antibacterial, anti-inflammatory, and hormone regulation, while ARH and ARB indicated more haemostasis, blood enrichment, neuromodulation, neuroprotection and tranquilisation, hepatoprotection, and antitumour activities than that of ART. The ARHs, ARBs, and ARTs can also be distinguished from each other based on the endophytic fungi at the microbiome level. The most dominant endophytic fungi were distributed in ART; the differences between ART and ARH were the largest, and ARB was in a transition state, which is consistent with the metabolite distributions. Structural equation modelling showed that the endophytic fungi were highly indicative of the metabolic components. Correlation analysis further identified the endophytic fungi significantly positively correlated with important active components, including Condenascus tortuosus, Sodiomyces alcalophilus, and Pleotrichocladium opacum. The bidirectional multivariate interactions between endophytic fungi and the metabolic components shape their spatial variations along the longitudinal direction in the Ang root.

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.

Effect of curcumin on malignant hepatocytes and mitochondria studied using atomic force microscopy.

Mitochondria are emerging as potential targets for the cancer treatment. In this study, the effects of curcumin on the activity, migration, and mitochondrial membrane potential (MMP) of malignant hepatocytes (SMMC-7721 cells) were determined using cell viability, migration, and MMP assays. Changes in the morphology and biomechanics of SMMC-7721 cells and their mitochondria were studied using both optical microscopy and atomic force microscopy (AFM). The cell survival rate, migration and MMP depended on the concentration of curcumin. Optical microscopy studies showed that curcumin altered the cell morphology. AFM studies showed that the changes in the morphology and nanomechanics of SMMC-7721 cells and their mitochondria, were induced by curcumin. As the concentration of curcumin increased, the cell length, width, and adhesion decreased, but the height, roughness and Young's modulus increased. In contrast, the mitochondrial length, width, height and roughness increased, but the adhesion and Young's modulus decreased. There was a close relationship between mitochondria and cells in terms of function, morphology and biomechanics. This study shows the effects of curcumin on SMMC-7721 cells and their mitochondria from biology and biophysics perspectives. The findings aid in comprehensively understanding the interactions between mitochondria and malignant hepatocytes.

Structural insights into the co-aggregation of Aß and tau amyloid core peptides: Revealing potential pathological heterooligomers by simulations.

The self-aggregation of amyloid-ß (Aß) and tau proteins are closely implicated in Alzheimer's disease (AD). Recent evidence indicates that Aß and tau proteins can cross-interact to form co-aggregates, which aggravates the development of AD. However, their transient heterooligomer conformations and co-aggregation molecular mechanisms are largely unknown. Herein, we utilize replica exchange molecular dynamics simulations to investigate the conformational ensembles formed by the central hydrophobic core of Aß (Aß16-22) and each of two fibril-nucleating core segments of tau (PHF6* and PHF6). Both PHF6 and PHF6* are found to co-aggregate with Aß16-22 into ß-sheet-rich heterooligomers. Intriguingly, PHF6 and Aß16-22 peptides formed closed ß-barrels, while PHF6* and Aß16-22 formed open ß-barrels, implying their distinct co-aggregation property. Compared to Aß16-22-PHF6*, Aß16-22-PHF6 heterooligomers have higher ß-sheet content, and contain longer ß-strands and larger ß-sheets, indicative of stronger co-aggregation ability of PHF6 with Aß16-22. Further analyses reveal that hydrophobic and π-π stacking interactions between Y310 of PHF6 and Aß16-22 are crucial for the closed ß-barrel/larger ß-sheet formation in Aß16-22-PHF6 heterooligomers. These results highlight the paramount importance of PHF6 fragment, particularly Y310 residue, as a potential target for inhibiting Aß-tau co-aggregation, which could help for effective therapeutic design in mitigating Aß-tau co-aggregation related amyloidogenesis.

TMSC-m7G: A transformer architecture based on multi-sense-scaled embedding features and convolutional neural network to identify RNA N7-methylguanosine sites.

RNA N7-methylguanosine (m7G) is a crucial chemical modification of RNA molecules, whose principal duty is to maintain RNA function and protein translation. Studying and predicting RNA N7-methylguanosine sites aid in comprehending the biological function of RNA and the development of new drug therapy regimens. In the present scenario, the efficacy of techniques, specifically deep learning and machine learning, stands out in the prediction of RNA N7-methylguanosine sites, leading to improved accuracy and identification efficiency. In this study, we propose a model leveraging the transformer framework that integrates natural language processing and deep learning to predict m7G sites, called TMSC-m7G. In TMSC-m7G, a combination of multi-sense-scaled token embedding and fixed-position embedding is used to replace traditional word embedding for the extraction of contextual information from sequences. Moreover, a convolutional layer is added in the encoder to make up for the shortage of local information acquisition in transformer. The model's robustness and generalization are validated through 10-fold cross-validation and an independent dataset test. Results demonstrate outstanding performance in comparison to the most advanced models available. Among them, the Accuracy of TMSC-m7G reaches 98.70% and 92.92% on the benchmark dataset and independent dataset, respectively. To facilitate the popularization and use of the model, we have developed an intuitive online prediction tool, which is easily accessible for free at http://39.105.212.81/.

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.

iAVPs-ResBi: Identifying antiviral peptides by using deep residual network and bidirectional gated recurrent unit.

Human history is also the history of the fight against viral diseases. From the eradication of viruses to coexistence, advances in biomedicine have led to a more objective understanding of viruses and a corresponding increase in the tools and methods to combat them. More recently, antiviral peptides (AVPs) have been discovered, which due to their superior advantages, have achieved great impact as antiviral drugs. Therefore, it is very necessary to develop a prediction model to accurately identify AVPs. In this paper, we develop the iAVPs-ResBi model using k-spaced amino acid pairs (KSAAP), encoding based on grouped weight (EBGW), enhanced grouped amino acid composition (EGAAC) based on the N5C5 sequence, composition, transition and distribution (CTD) based on physicochemical properties for multi-feature extraction. Then we adopt bidirectional long short-term memory (BiLSTM) to fuse features for obtaining the most differentiated information from multiple original feature sets. Finally, the deep model is built by combining improved residual network and bidirectional gated recurrent unit (BiGRU) to perform classification. The results obtained are better than those of the existing methods, and the accuracies are 95.07, 98.07, 94.29 and 97.50% on the four datasets, which show that iAVPs-ResBi can be used as an effective tool for the identification of antiviral peptides. The datasets and codes are freely available at https://github.com/yunyunliang88/iAVPs-ResBi.

High-Performance and Low-Power p-Channel Transistors Based on Monolayer Be2C.

The advantages of 2D materials in alleviating the issues of short-channel effect and power dissipation in field-effect transistors (FETs) are well recognized. However, the progress of complementary integrated circuits has been stymied by the absence of high-performance (HP) and low-power (LP) p-channel transistors. Therefore, we conducted an investigation into the electronic and ballistic transport characteristics of monolayer Be2C, which features quasi-planar hexacoordinate carbons, by employing nonequilibrium Green's function combined with density functional theory. Be2C monolayer has planar anticonventional bonds and a direct bandgap of 1.53 eV. The Ion of p-type Be2C HP FETs can achieve a remarkable 2767 µA µm-1. All of the device properties of 2D Be2C FETs can exceed the demands of the International Roadmap for Devices and Systems. The excellent properties of Be2C as a 2D p-orbital material with a high hole mobility are discussed from different aspects. Our findings thus illustrate the tremendous potential of 2D Be2C for the next generation of HP and LP electronics applications.

Eco-Friendly Zn-Ag-In-Ga-S Quantum Dots: Amorphous Indium Sulfide Passivated Silver/Sulfur Vacancies Achieving Efficient Red Light-Emitting Diodes.

I-III-VI quantum dots (QDs) and derivatives (I, III, and VI are Ag+/Cu+, Ga3+/In3+, and S2-/Se2-, respectively) are the ideal candidates to replace II-VI (e.g., CdSe) and perovskite QDs due to their nontoxicity, pure color, high photoluminescence quantum yield (PLQY), and full visible coverage. However, the chaotic cation alignment in multielement systems can easily lead to the formation of multiple surface vacancies, highlighted as VI and VVI, leading to nonradiative recombination and nonequilibrium carrier distribution, which severely limit the performance improvement of materials and devices. Here, based on Zn-Ag-In-Ga-S QDs, we construct an ultrathin indium sulfide shell that can passivate electron vacancies and convert donor/acceptor level concentrations. The optimized In-rich 2-layer indium sulfide structure not only enhances the radiative recombination rate by preventing further VS formation but also achieves the typical DAP emission enhancement, achieving a significant increase in PLQY to 86.2% at 628 nm. Moreover, the optimized structure can mitigate the lattice distortion and make the carrier distribution in the interior of the QDs more balanced. On this basis, red QD light-emitting diodes (QLEDs) with the highest external quantum efficiency (EQE; 5.32%) to date were obtained, providing a novel scheme for improving I-III-VI QD-based QLED efficiency.