Evaluation of volatile components from the tuber, fibrous roots, bud, stem and leaf tissues of Bletilla striata for its anti-colon cancer activity.

Bletilla striata (Thunb.) Rchb.f., a medicinal plant in the Orchidaceae family, is mainly found in East Asia and has extensive pharmacological activities. Plant's volatile components are important active ingredients with a wide range of physiological activities, and B. striata has a special odor and unique volatile components. Yet it has received little attention, hindering a full understanding of its phytochemical components. Employing the ultrasonic-assisted extraction method, the volatile components of B. striata's fibrous root, bud, aerial part and tuber were extracted, resulting in yields of 0.06%, 0.64%, 3.38% and 4.47%, respectively. A total of 78 compounds were identified from their chemical profiles using gas chromatography-mass spectrometry (GC-MS), including 45 components with the main compounds of linoleic acid (content accounting for 31.23%), n-hexadecanoic acid (13.53%), and octadecanoic acid (9.5%) from the tuber, 34 components with the main compounds of eicosane, 2-methyl- (28.42%), linoelaidic acid (10.43%), linoleic acid (4.53%), and n-hexadecanoic acid (6.91%) from the fibrous root, 38 components with the main compounds of pentadeca-6,9-dien-1-ol (9.29%), n-hexadecanoic acid (11%), eicosane,2-methyl- (23.43%), and linoleic acid (23.53%) from the bud, and 27 components with the main compounds of linoelaidic acid (5.97%), n-hexadecanoic acid (15.99%), and linolenic acid ethyl ester (18.9%) from the aerial part. Additionally, the growth inhibition activity against colon cancer HCT116 cells was evaluated using sulforhodamine B (SRB) assay and the thiazolyl blue tetrazolium bromide (MTT) assay, and the accumulation of reactive oxygen species (ROS) was determined using dichloro-dihydro-fluorescein diacetate (DCFH-DA) staining and fluorescence intensity analysis. The volatile extracts exhibited significant growth inhibitory efficacy against HCT116 cells, with half-maximal inhibitory concentration (IC50) values of 3.65, 2.32, 2.42 and 3.89 mg/mL in the SRB assay, and 3.55, 2.58, 3.12 and 4.80 mg/mL in the MTT assay for the root, bud, aerial part, and tuber, respectively. Notably, treatment with the aerial part extract caused morphological changes in the cells and significantly raised the intracellular ROS level. In summary, the chemical profiles of the volatile components of B. striata were revealed for the first time, demonstrating a certain tissue specificity. Additionally, it demonstrated for the first time that these volatile extracts possess potent anti-colon cancer activity, highlighting the importance of these volatile components in B. striata's medicinal properties.

BindingSiteDTI: Differential-scale binding site modelling for Drug-Target interaction prediction.

MOTIVATION: Enhanced by contemporary computational advances, the prediction of drug-target interactions (DTIs) has become crucial in developing de novo and effective drugs. Existing deep learning approaches to DTI prediction are frequently beleaguered by a tendency to overfit specific molecular representations, which significantly impedes their predictive reliability and utility in novel drug discovery contexts. Furthermore, existing DTI networks often disregard the molecular size variance between macro molecules (targets) and micro molecules (drugs) by treating them at an equivalent scale that undermines the accurate elucidation of their interaction. RESULTS: We propose a novel DTI network with a differential-scale scheme to model the binding site for enhancing DTI prediction, which is named as BindingSiteDTI. It explicitly extracts multiscale substructures from targets with different scales of molecular size and fixed-scale substructures from drugs, facilitating the identification of structurally similar substructural tokens, and models the concealed relationships at the substructural level to construct interaction feature. Experiments conducted on popular benchmarks, including DUD-E, human and BindingDB, shown that BindingSiteDTI contains significant improvements compared with recent DTI prediction methods. AVAILABILITY: The source code of BindingSiteDTI can be accessed at https://github.com/MagicPF/BindingSiteDTI. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Association of fetal fraction and cell-free fetal DNA with adverse pregnancy outcomes: A systematic review.

BACKGROUND: Adverse pregnancy outcomes, which can be caused by multiple factors, present a significant threat to the health of mothers and their babies. Cell-free fetal DNA (cffDNA) from placental trophoblast cells might be able to reflect placental and fetal status. Previous studies have yielded controversial results regarding the association of FF or cffDNA with various adverse pregnancy outcomes. A previous study has attempted to systematically assess the association between low fetal fraction (FF) and adverse pregnancy outcomes, but it failed to perform quantitative analyses due to the few studies included. In the present study, we attempted to quantitatively assess the association of FF (or cffDNA) with adverse pregnancy outcomes and further analyze the causes of heterogeneity. OBJECTIVES: To investigate the association of high/low FF or cffDNA with adverse pregnancy outcomes. SEARCH STRATEGY: We searched the databases of PubMed, Embase, Cochrane, and Web of Science from January 1, 1990, to June 15, 2022 in this meta-analysis. SELECTION CRITERIA: Studies on the relationships of adverse pregnancy outcomes in women with FF or cell free DNA were included. Non-English literature was excluded. DATA COLLECTION AND ANALYSIS: Data about pregnancy outcomes and cell free DNA were extracted and meta-analyzed. Subgroup analysis was performed by different outcomes. MAIN RESULTS: There were 11 studies included involving 8280 participants. No significant heterogeneity was observed among the studies (I2 = 27%, 25%), and a fixed-effect model was used for weighted quantitative analysis. The results revealed that the FF or cffDNA during pregnancy was significantly associated with adverse pregnancy outcomes in pregnant women (OR = 1.57, 95% CI [1.24, 1.99], P = 0.233). The overall incidence of the maternal adverse outcomes was 8% (95% CI: 5-13). Subgroup analysis of different outcomes showed an evident association between low FF or cffDNA and hypertensive disorders of pregnancy (HDP) (OR = 1.76, 95% CI [1.36, 2.27], P = 0.581). There was no evidence that the occurrence of spontaneous preterm birth (sPTB) and placental abnormality was associated with FF or cffDNA. No association was observed between low FF or cffDNA during pregnancy and adverse outcomes in fetuses (OR = 1.39, 95% CI [0.99, 1.94], P = 0.242). The overall incidence of adverse outcomes in fetuses was 8% (95% CI: 6-11). There were controversies over the association between high FF or cffDNA and HDP, and sPTB and small for gestational age infant, among different studies. CONCLUSIONS: Pregnant women with low FF or cffDNA during the first or second trimester of pregnancy have an overall increased risk of adverse pregnancy outcomes, especially HDP. However, the association between FF and various pregnancy outcomes needs to be further explored by more prospective studies.

Antiferromagnetic magnonic charge current generation via ultrafast optical excitation.

Néel spin-orbit torque allows a charge current pulse to efficiently manipulate the Néel vector in antiferromagnets, which offers a unique opportunity for ultrahigh density information storage with high speed. However, the reciprocal process of Néel spin-orbit torque, the generation of ultrafast charge current in antiferromagnets has not been demonstrated. Here, we show the experimental observation of charge current generation in antiferromagnetic metallic Mn2Au thin films using ultrafast optical excitation. The ultrafast laser pulse excites antiferromagnetic magnons, resulting in instantaneous non-equilibrium spin polarization at the antiferromagnetic spin sublattices with broken spatial symmetry. Then the charge current is generated directly via spin-orbit fields at the two sublattices, which is termed as the reciprocal phenomenon of Néel spin-orbit torque, and the associated THz emission can be detected at room temperature. Besides the fundamental significance on the Onsager reciprocity, the observed magnonic charge current generation in antiferromagnet would advance the development of antiferromagnetic THz emitter.

Analysis of the effects of M2 macrophage-derived PDE4C on the prognosis, metastasis and immunotherapy benefit of osteosarcoma.

Tumour-associated macrophages (TAMs), encompassing M1 and M2 subtypes, exert significant effects on osteosarcoma (OS) progression and immunosuppression. However, the impacts of TAM-derived biomarkers on the progression of OS remains limited. The GSE162454 profile was subjected to single-cell RNA (scRNA) sequencing analysis to identify crucial mediators between TAMs and OS cells. The clinical features, effects and mechanisms of these mediators on OS cells and tumour microenvironment were evaluated via biological function experiments and molecular biology experiments. Phosphodiesterase 4C (PDE4C) was identified as a pivotal mediator in the communication between M2 macrophages and OS cells. Elevated levels of PDE4C were detected in OS tissues, concomitant with M2 macrophage level, unfavourable prognosis and metastasis. The expression of PDE4C was observed to increase during the conversion process of THP-1 cells to M2 macrophages, which transferred the PDE4C mRNA to OS cells through exosome approach. PDE4C increased OS cell proliferation and mobility via upregulating the expression of collagens. Furthermore, a positive correlation was observed between elevated levels of PDE4C and increased TIDE score, decreased response rate following immune checkpoint therapy, reduced TMB and diminished PDL1 expression. Collectively, PDE4C derived from M2 macrophages has the potential to enhance the proliferation and mobility of OS cells by augmenting collagen expression. PDE4C may serve as a valuable biomarker for prognosticating patient outcomes and response rates following immunotherapy.

Fe3+-activated magnetoplumbite persistent luminescence phosphor by modulating the oxygen vacancy.

Broadband near-infrared (NIR) spectroscopy has gained significant attention due to its versatile application in various fields. In the realm of NIR phosphors, Fe3+ ion is an excellent activator known for its nontoxic and harmless nature. In this study, we prepared an Fe3+-activated SrGa12O19 (SGO) NIR phosphor and analyzed its phase and luminescence properties. Upon excitation at 326 nm, the SGO:Fe3+ phosphor exhibited a broadband emission in the range 700-1000 nm, peaking at 816 nm. The optical band gap of SGO:Fe3+ was evaluated. To enhance the long-lasting phosphorescence, an oxygen vacancy-rich SGO:Fe3+ (VO-SGO:Fe3+) sample was prepared for activation. Interestingly, the increase in the oxygen-vacancy concentration indeed contributed to the activation of persistent luminescence of Fe3+ ions. The VO-SGO:Fe3+ sample has a long duration and high charge storage capacity, allowing it to perform efficiently in various applications. This work provides the foundation for further design of Cr3+-free PersL phosphors with efficient NIR PersL.

The epigenetic regulators EP300/CREBBP represent promising therapeutic targets in MLL-rearranged acute myeloid leukemia.

Acute myeloid leukemia (AML) with mixed-lineage leukemia (MLL) gene rearrangements (MLL-r) is an aggressive subtype of blood cancer with dismal prognosis, underscoring the urgent need for novel therapeutic strategies. E1A-binding protein (EP300) and CREB-binding protein (CREBBP) function as essential transcriptional coactivators and acetyltransferases, governing leukemogenesis through diverse mechanisms. Targeting EP300/CREBBP holds great promise for treating leukemia with some certain cytogenetic abnormalities. Here, we demonstrated that EP300 and CREBBP are core epigenetic regulators in the pathogenesis of MLL-r AML through assaying the transposase-accessible chromatin with high-throughput sequencing (ATAC-seq). Knocking-out EP300/CREBBP and inhibitor (A-485) treatment depressed the MLL-r cells proliferation, while the MLL wild-type cells remained uninfluenced. We found that the CDK4/RB/E2F axis was downregulated specifically in MLL-r AML cell after A-485 treatment by RNA-seq, western blot and cut-tag analyses. EP300/CREBBP inhibitor selectively exerted potent anti-leukemia activity through blocking the MLL-r-BET complex binding to H3K27Ac modification on critical genes loci, distinct from global histone acetylation. Collectively, our study identified EP300/CREBBP as a critical epigenetic driver of MLL-r leukemia and validated their therapeutic potential through targeting inhibition, offering a promising avenue for improving clinical outcomes in this aggressive leukemia.

Dual-Mode Patterns Enabled by Photofluidization of an Azobenzene-Containing Linear Liquid Crystal Copolymer.

Creating dual-mode patterns in the same area of the material is an advanced method to increase the dimension of information storage, improve the level of encryption security, and promote the development of encoding technology. However, in situ, different patterns may lead to serious mutual interference in the process of manufacturing and usage. New materials and patterning techniques are essential for the advancement of noninterfering dual-mode patterns. Herein, noninterfering dual-mode patterns are demonstrated by combining the structural color and chromatic polarization, which is designed with an azobenzene-containing linear liquid crystal copolymer featuring a photofluidization effect. On the one hand, structural color patterns are imprinted via silicon templates with periodic microstructures after a UV-light-induced local transition of the polymer surface from a glassy to rubbery state. On the other hand, different polarization patterns based on the local photoinduced orientation of mesogens are created within the photofluidized region by the Weigert effect. Especially, the secondary imprinting is used to eliminate the partial damage to the structural color patterns during writing of the polarization patterns, thus obtaining dual-mode patterns without interference. This study provides a blueprint for the creation of advanced materials and sophisticated photopatterning techniques with potential cross-industry applications.

Bone mineral density as an individual prognostic biomarker in NSCLC patients treated with immune checkpoint inhibitors.

Background: Immune checkpoint inhibitors (ICIs) have left a deep impression in the treatment of non-small cell lung cancer (NSCLC), however, not all patients benefit from it. The purpose of this study was to investigate the prognostic value of baseline bone mineral density (BMD) derived from chest computed tomography (CT) scans in NSCLC patients treated with ICIs. Methods: This study included patients with advanced NSCLC who underwent ICI treatment at the Wuhan Union Hospital from March 2020 to October 2022. Baseline BMD was evaluated at non-contrast chest CT at the level of first lumbar vertebra. Patients were divided into BMD-lower group and BMD-higher group according to the optimal cutoff value calculated by X-tile software. Baseline characteristics of the two groups were compared and variables between the two groups were balanced by propensity score matching (PSM) analysis. We calculated the objective response rate (ORR) and disease control rate (DCR) of the two groups and analyzed overall survival (OS) and progression-free survival (PFS) using BMD and other clinical indexes through Cox regression models and Kaplan-Meier survival curves. Results: A total of 479 patients were included in this study, and all patients were divided into BMD-lower group (n=270) and BMD-higher group (n=209). After PSM analysis, each group consisted of 150 patients. ORR (43.3% vs. 43.5% before PSM, P = 0.964; 44.7% vs. 44.7% after PSM, P = 1.000) and DCR (91.1% vs. 94.3% before PSM, P = 0.195; 93.3% vs. 96.7% after PSM, P =0.190) were similar in two groups. There was no statistically significant relationship between BMD degree and PFS before (16.0 months vs. 18.0 months, P = 0.067) and after PSM analysis (17.0 months vs. 19.0 months, P = 0.095). However, lower BMD was associated with shorter OS both before (20.5 months vs. 23.0 months, P< 0.001) and after PSM analysis (20.0 months vs. 23.0 months, P = 0.008). Conclusion: Lower baseline BMD is associated with worse clinical outcomes in NSCLC patients treated with ICIs. As a reliable and easily obtained individual prognostic biomarker, BMD can become a routine detection indicator before immunotherapy.

Tensor Network Message Passing.

When studying interacting systems, computing their statistical properties is a fundamental problem in various fields such as physics, applied mathematics, and machine learning. However, this task can be quite challenging due to the exponential growth of the state space as the system size increases. Many standard methods have significant weaknesses. For instance, message-passing algorithms can be inaccurate and even fail to converge due to short loops, while tensor network methods can have exponential computational complexity in large graphs due to long loops. In this Letter, we propose a new method called "tensor network message passing." This approach allows us to compute local observables like marginal probabilities and correlations by combining the strengths of tensor networks in contracting small subgraphs with many short loops and the strengths of message-passing methods in globally sparse graphs, thus addressing the crucial weaknesses of both approaches. Our algorithm is exact for systems that are globally treelike and locally dense-connected when the dense local graphs have a limited tree width. We have conducted numerical experiments on synthetic and real-world graphs to compute magnetizations of Ising models and spin glasses, and have demonstrated the superiority of our approach over standard belief propagation and the recently proposed loopy message-passing algorithm. In addition, we discuss the potential applications of our method in inference problems in networks, combinatorial optimization problems, and decoding problems in quantum error correction.

Solution-phase sample-averaged single-particle spectroscopy of quantum emitters with femtosecond resolution.

The development of many quantum optical technologies depends on the availability of single quantum emitters with near-perfect coherence. Systematic improvement is limited by a lack of understanding of the microscopic energy flow at the single-emitter level and ultrafast timescales. Here we utilize a combination of fluorescence correlation spectroscopy and ultrafast spectroscopy to capture the sample-averaged dynamics of defects with single-particle sensitivity. We employ this approach to study heterogeneous emitters in two-dimensional hexagonal boron nitride. From milliseconds to nanoseconds, the translational, shelving, rotational and antibunching features are disentangled in time, which quantifies the normalized two-photon emission quantum yield. Leveraging the femtosecond resolution of this technique, we visualize electron-phonon coupling and discover the acceleration of polaronic formation on multi-electron excitation. Corroborated with theory, this translates to the photon fidelity characterization of cascaded emission efficiency and decoherence time. Our work provides a framework for ultrafast spectroscopy in heterogeneous emitters, opening new avenues of extreme-scale characterization for quantum applications.

Insulin-like growth factor-1 promotes the testicular sperm production by improving germ cell survival and proliferation in high-fat diet-treated male mice.

BACKGROUND: A decrease in semen volume among men is comparable to the rising prevalence of obesity worldwide. The anabolic hormone insulin-like growth factor-1 (IGF-1) can promote proliferation and differentiation in cultured mouse spermatogonial stem cells and alleviate abnormal in vitro spermatogenesis. Additionally, serum IGF-1 level is negatively correlated with body mass index. Whereas the role of IGF-1 in the sperm production in obese men remains unclear. OBJECTIVE: To investigate the therapeutic effect and potential mechanism of IGF-1 on spermatogenesis of high-fat diet (HFD)-induced obesity mice. METHODS: An HFD-induced obesity mouse model was established. Alterations in testicular morphology, sperm count, proliferation, and apoptosis were observed by H&E staining,immunohistochemistry, immunofluorescence, and Western blotting. Exogenous recombinant IGF-1 was administered to obese mice to investigate the correlations between altered testicular IGF-1 levels and sperm production. RESULTS: The sperm count was reduced, the testicular structure was disordered, and sex hormone levels were abnormal in HFD-fed mice compared with normal diet-fed mice. The expression of proliferation-related antigens such as proliferating cell nuclear antigen (PCNA) and Ki-67 was decreased, while that of proapoptotic proteins such as c-caspase3 was increased in testes from HFD-fed mice. Most importantly, the phosphorylation of insulin-like growth factor-1 receptor (IGF-1R) in testes was decreased due to reductions in IGF-1 from hepatocytes and Sertoli cells. Recombinant IGF-1 alleviated these functional impairments by promoting IGF-1R, Akt, and Erk1/2 phosphorylation in the testes. CONCLUSIONS: Insufficient IGF-1/IGF-1R signaling is intimately linked to damaged sperm production in obese male mice. Exogenous IGF-1 can improve survival and proliferation as well as sperm production. This study provides a novel theoretical basis and a target for the treatment of obese men with oligozoospermia.

Screening for urothelial carcinoma cells in urine based on digital holographic flow cytometry through machine learning and deep learning methods.

The incidence of urothelial carcinoma continues to rise annually, particularly among the elderly. Prompt diagnosis and treatment can significantly enhance patient survival and quality of life. Urine cytology remains a widely-used early screening method for urothelial carcinoma, but it still has limitations including sensitivity, labor-intensive procedures, and elevated cost. In recent developments, microfluidic chip technology offers an effective and efficient approach for clinical urine specimen analysis. Digital holographic microscopy, a form of quantitative phase imaging technology, captures extensive data on the refractive index and thickness of cells. The combination of microfluidic chips and digital holographic microscopy facilitates high-throughput imaging of live cells without staining. In this study, digital holographic flow cytometry was employed to rapidly capture images of diverse cell types present in urine and to reconstruct high-precision quantitative phase images for each cell type. Then, various machine learning algorithms and deep learning models were applied to categorize these cell images, and remarkable accuracy in cancer cell identification was achieved. This research suggests that the integration of digital holographic flow cytometry with artificial intelligence algorithms offers a promising, precise, and convenient approach for early screening of urothelial carcinoma.

Phospholipid Type Regulates Protein Corona Composition and In Vivo Performance of Lipid Nanodiscs.

Over the years, there has been significant interest in PEGylated lipid-based nanocarriers within the drug delivery field. The inevitable interplay between the nanocarriers and plasma protein plays a pivotal role in their in vivo biological fate. Understanding the factors influencing lipid-based nanocarrier and protein corona interactions is of paramount importance in the design and clinical translation of these nanocarriers. Herein, discoid-shaped lipid nanodiscs (sNDs) composed of different phospholipids with varied lipid tails and head groups were fabricated. We investigated the impact of phospholipid components on the interaction between sNDs and serum proteins, particle stability, and biodistribution. The results showed that all of these lipid nanodiscs remained stable over a 15 day storage period, while their stability in the blood serum demonstrated significant differences. The sND composed of POPG exhibited the least stability due to its potent complement activation capability, resulting in rapid blood clearance. Furthermore, a negative correlation between the complement activation capability and serum stability was identified. Pharmacokinetic and biodistribution experiments indicated that phospholipid composition did not influence the capability of sNDs to evade the accelerated blood clearance phenomenon. Complement deposition on the sND was inversely associated with the area under the curve. Additionally, all lipid nanodiscs exhibited dominant adsorption of apolipoprotein. Remarkably, the POPC-based lipid nanodisc displayed a significantly higher deposition of apolipoprotein E, contributing to an obvious brain distribution, which provides a promising tool for brain-targeted drug delivery.

Non-small cell lung cancer and immune checkpoint inhibitor therapy: does non-alcoholic fatty liver disease have an effect?

BACKGROUND: Immunotherapy based on the application of immune checkpoint inhibitors (ICIs) is one of the standard treatments for advanced non-small cell lung cancer (NSCLC). Non-alcoholic fatty liver Disease (NAFLD) has demonstrated predictive value for response to immunotherapy in non-lung cancer types. Our study investigated the effect of NAFLD on the efficacy of real-life use of ICIs for patients with stage III / IV NSCLC. METHODS: The clinical and imaging data of patients with stage III / IV NSCLC who were first admitted to Union Hospital, Tongji Medical College, Huazhong University of Science and Technology from March 2020 to July 2022 were retrospectively collected to ensure that they underwent at least one CT scan before treatment. A total of 479 patients were divided into the NAFLD group (Liver/Spleen density ratio ≤ 1) and the non-NAFLD group (Liver/Spleen density ratio > 1) by measuring the baseline liver and spleen CT value. The overall survival (OS), progression-free survival (PFS), objective response rate (ORR) and disease control rate (DCR) of the patients were obtained. RESULTS: A total of 118 patients with NAFLD and 361 patients without NAFLD were included in the study. Patients with NAFLD tended to have higher BMI and higher total bilirubin compared to patients without NAFLD. The median duration of follow-up in the study was 22 m (IQR, 17-29 m). Both of 2 groups had a higher DCR (94% vs. 92%, p = 0.199) and ORR (38.1% vs. 44.9%, p = 0.452) respectively. There was no difference in efficacy between the two groups. In univariate analysis, NAFLD had no significant effect on PFS (p = 0.785) and OS (p = 0.851). Surprisingly, the presence of hypertension was observed to be associated with a higher OS (HR 1.471 95%CI 1.018-2.127, p = 0.040). Besides, based on multivariate analysis, lactic dehydrogenase was associated with PFS (HR 1.001 95%CI 1.000,1.002, p = 0.037) and OS (HR 1.002, 95%CI 1.001-1.003, p < 0.001). CONCLUSIONS: Among patients with NSCLC, NAFLD did not result in changes in survival or disease progression after immune checkpoint inhibitor therapy.

Data-Driven Discovery of Gas-Selective Organic Linkers in Metal-Organic Frameworks for the Separation of Ethylene and Ethane.

Metal-organic frameworks (MOFs) are potential candidates for gas-selective adsorbents for the separation of an ethylene/ethane mixture. To accelerate material discovery, high-throughput computational screening is a viable solution. However, classical force fields, which were widely employed in recent studies of MOF adsorbents, have been criticized for their failure to cover complicated interactions such as those involving π electrons. Herein, we demonstrate that machine learning force fields (MLFFs) trained on quantum-chemical reference data can overcome this difficulty. We have constructed a MLFF to accurately predict the adsorption energies of ethylene and ethane on the organic linkers of MOFs and discovered that the π electrons from both the ethylene molecule and the aromatic rings in the linkers could substantially influence the selectivity for gas adsorption. Four kinds of MOF linkers are identified as having promise for the separation of ethylene and ethane, and our results could also offer a new perspective on the design of MOF building blocks for diverse applications.

Influence of Li Content on the Topological Inhibition of Oxygen Loss in Li-Rich Cathode Materials.

Lithium-rich layer oxide cathodes are promising energy storage materials due to their high energy densities. However, the oxygen loss during cycling limits their practical applications. Here, the essential role of Li content on the topological inhibition of oxygen loss in lithium-rich cathode materials and the relationship between the migration network of oxygen ions and the transition metal (TM) component are revealed. Utilizing first-principles calculations in combination with percolation theory and Monte Carlo simulations, it is found that TM ions can effectively encage the oxidized oxygen species when the TM concentration in TM layer exceeds 5/6, which hinders the formation of a percolating oxygen migration network. This study demonstrates the significance of rational compositional design in lithium-rich cathodes for effectively suppressing irreversible oxygen release and enhancing cathode cycling performance.

Electrical-Controllable Antiferromagnet-Based Tunnel Junction.

An electrical-controllable antiferromagnet tunnel junction is a key goal in spintronics, holding immense promise for ultradense and ultrastable antiferromagnetic memory with high processing speed for modern information technology. Here, we have advanced toward this goal by achieving an electrical-controllable antiferromagnet-based tunnel junction of Pt/Co/Pt/Co/IrMn/MgO/Pt. The exchange coupling between antiferromagnetic IrMn and Co/Pt perpendicular magnetic multilayers results in the formation of an interfacial exchange bias and exchange spring in IrMn. Encoding information states "0" and "1" is realized through the exchange spring in IrMn, which can be electrically written by spin-orbit torque switching with high cyclability and electrically read by antiferromagnetic tunneling anisotropic magnetoresistance. Combining spin-orbit torque switching of both exchange spring and exchange bias, a 16 Boolean logic operation is successfully demonstrated. With both memory and logic functionalities integrated into our electrically controllable antiferromagnetic-based tunnel junction, we chart the course toward high-performance antiferromagnetic logic-in-memory.

Crystal Structure Assignment for Unknown Compounds from X-ray Diffraction Patterns with Deep Learning.

Determining the structures of previously unseen compounds from experimental characterizations is a crucial part of materials science. It requires a step of searching for the structure type that conforms to the lattice of the unknown compound, which enables the pattern matching process for characterization data, such as X-ray diffraction (XRD) patterns. However, this procedure typically places a high demand on domain expertise, thus creating an obstacle for computer-driven automation. Here, we address this challenge by leveraging a deep-learning model composed of a union of convolutional residual neural networks. The accuracy of the model is demonstrated on a dataset of over 60,000 different compounds for 100 structure types, and additional categories can be integrated without the need to retrain the existing networks. We also unravel the operation of the deep-learning black box and highlight the way in which the resemblance between the unknown compound and a structure type is quantified based on both local and global characteristics in XRD patterns. This computational tool opens new avenues for automating structure analysis on materials unearthed in high-throughput experimentation.