Unlocking Li superionic conductivity in face-centred cubic oxides via face-sharing configurations.

Oxides with a face-centred cubic (fcc) anion sublattice are generally not considered as solid-state electrolytes as the structural framework is thought to be unfavourable for lithium (Li) superionic conduction. Here we demonstrate Li superionic conductivity in fcc-type oxides in which face-sharing Li configurations have been created through cation over-stoichiometry in rocksalt-type lattices via excess Li. We find that the face-sharing Li configurations create a novel spinel with unconventional stoichiometry and raise the energy of Li, thereby promoting fast Li-ion conduction. The over-stoichiometric Li-In-Sn-O compound exhibits a total Li superionic conductivity of 3.38 × 10-4 S cm-1 at room temperature with a low migration barrier of 255 meV. Our work unlocks the potential of designing Li superionic conductors in a prototypical structural framework with vast chemical flexibility, providing fertile ground for discovering new solid-state electrolytes.

Large-Area Layered Membranes with Precisely Controlled Nano-Confined Channels.

Two-dimensional (2D) nanosheets-based membranes, which have controlled 2D nano-confined channels, are highly desirable for molecular/ionic sieving and confined reactions. However, it is still difficult to develop an efficient method to prepare large-area membranes with high stability, high orientation, and accurately adjustable interlayer spacing. Here, we present a strategy to produce metal ion cross-linked membranes with precisely controlled 2D nano-confined channels and high stability in different solutions using superspreading shear-flow-induced assembly strategy. For example, membranes based on graphene oxide (GO) exhibit interlayer spacing ranging from 8.0 ± 0.1 Å to 10.3 ± 0.2 Å, with a precision of down to 1 Å. At the same time, the value of the orientation order parameter (f) of GO membranes is up to 0.95 and GO membranes exhibit superb stability in different solutions. The strategy we present, which can be generalized to the preparation of 2D nano-confined channels based on a variety of 2D materials, will expand the application scope and provide better performances of membranes.

Lithium superionic conductors with corner-sharing frameworks.

Superionic lithium conductivity has only been discovered in a few classes of materials, mostly found in thiophosphates and rarely in oxides. Herein, we reveal that corner-sharing connectivity of the oxide crystal structure framework promotes superionic conductivity, which we rationalize from the distorted lithium environment and reduced interaction between lithium and non-lithium cations. By performing a high-throughput search for materials with this feature, we discover ten new oxide frameworks predicted to exhibit superionic conductivity-from which we experimentally demonstrate LiGa(SeO3)2 with a bulk ionic conductivity of 0.11 mS cm-1 and an activation energy of 0.17 eV. Our findings provide insight into the factors that govern fast lithium mobility in oxide materials and will accelerate the development of new oxide electrolytes for all-solid-state batteries.

Promises and Challenges of Next-Generation "Beyond Li-ion" Batteries for Electric Vehicles and Grid Decarbonization.

The tremendous improvement in performance and cost of lithium-ion batteries (LIBs) have made them the technology of choice for electrical energy storage. While established battery chemistries and cell architectures for Li-ion batteries achieve good power and energy density, LIBs are unlikely to meet all the performance, cost, and scaling targets required for energy storage, in particular, in large-scale applications such as electrified transportation and grids. The demand to further reduce cost and/or increase energy density, as well as the growing concern related to natural resource needs for Li-ion have accelerated the investigation of so-called "beyond Li-ion" technologies. In this review, we will discuss the recent achievements, challenges, and opportunities of four important "beyond Li-ion" technologies: Na-ion batteries, K-ion batteries, all-solid-state batteries, and multivalent batteries. The fundamental science behind the challenges, and potential solutions toward the goals of a low-cost and/or high-energy-density future, are discussed in detail for each technology. While it is unlikely that any given new technology will fully replace Li-ion in the near future, "beyond Li-ion" technologies should be thought of as opportunities for energy storage to grow into mid/large-scale applications.

Cation-disordered rocksalt-type high-entropy cathodes for Li-ion batteries.

High-entropy (HE) ceramics, by analogy with HE metallic alloys, are an emerging class of solid solutions composed of a large number of species. These materials offer the benefit of large compositional flexibility and can be used in a wide variety of applications, including thermoelectrics, catalysts, superionic conductors and battery electrodes. We show here that the HE concept can lead to very substantial improvements in performance in battery cathodes. Among lithium-ion cathodes, cation-disordered rocksalt (DRX)-type materials are an ideal platform within which to design HE materials because of their demonstrated chemical flexibility. By comparing a group of DRX cathodes containing two, four or six transition metal (TM) species, we show that short-range order systematically decreases, whereas energy density and rate capability systematically increase, as more TM cation species are mixed together, despite the total metal content remaining fixed. A DRX cathode with six TM species achieves 307 mAh g-1 (955 Wh kg-1) at a low rate (20 mA g-1), and retains more than 170 mAh g-1 when cycling at a high rate of 2,000 mA g-1. To facilitate further design in this HE DRX space, we also present a compatibility analysis of 23 different TM ions, and successfully synthesize a phase-pure HE DRX compound containing 12 TM species as a proof of concept.

Elastic-Modulus-Dependent Macroscopic Supramolecular Assembly of Poly(dimethylsiloxane) for Understanding Fast Interfacial Adhesion.

Macroscopic supramolecular assembly (MSA) is a new concept of supramolecular science with an emphasis on noncovalent interactions between macroscopic building blocks with sizes exceeding 10 µm. Owing to a similar noncovalently interactive nature with the phenomena of bioadhesion, self-healing, etc. and flexible features in tailoring and designing modular building blocks, MSA has been developed as a simplified model to interpret interfacial phenomena and a facile method to fabricate supramolecular materials. However, at this early stage, MSA has always been limited to hydrogel materials, which provide flowability for high molecular mobility to the interfacial binding. The extension to a wide range of materials for MSA is desired. Herein, we have developed a strategy of adjusting intrinsic properties (e.g., elastic modulus) of nonhydrogel materials to realize MSA, which could broaden the material choices of MSA. Using the widely used elastomer of poly(dimethylsiloxane) (PDMS) as building blocks, we have demonstrated the elastic-modulus-dependent MSA of PDMS based on the host/guest molecular recognition between supramolecular groups of ß-cyclodextrin and adamantane. In the varied elastic modulus range of 0.38 to 3.84 MPa, we obtained the trend of the MSA probability decreasing from 100% at 0.38 MPa to 0% at 3.84 MPa. Meanwhile, in situ measurements of interactive forces between PDMS building blocks have supported the observed assembly phenomena. The underlying reasons are interpreted with the low-modulus flexible surfaces favoring for high molecular mobility to achieve interactions between multiple sites at the interface based on the theory of multivalency. Taken together, we have demonstrated the feasibility of directly adjusting the modulus of bulk materials to realize MSA of nonhydrogel materials, which may provide clues to the fast wet adhesion and new solutions to the additive manufacture of elastomer materials.

Differentiation of Pseudoprogression from True Progressionin Glioblastoma Patients after Standard Treatment: A Machine Learning Strategy Combinedwith Radiomics Features from T1-weighted Contrast-enhanced Imaging.

BACKGROUND: Based on conventional MRI images, it is difficult to differentiatepseudoprogression from true progressionin GBM patients after standard treatment, which isa critical issue associated with survival. The aim of this study was to evaluate the diagnostic performance of machine learning using radiomics modelfrom T1-weighted contrast enhanced imaging(T1CE) in differentiating pseudoprogression from true progression after standard treatment for GBM. METHODS: Seventy-sevenGBM patients, including 51 with true progression and 26 with pseudoprogression,who underwent standard treatment and T1CE, were retrospectively enrolled.Clinical information, including sex, age, KPS score, resection extent, neurological deficit and mean radiation dose, were also recorded collected for each patient. The whole tumor enhancementwas manually drawn on the T1CE image, and a total of texture 9675 features were extracted and fed to a two-step feature selection scheme. A random forest (RF) classifier was trained to separate the patients by their outcomes.The diagnostic efficacies of the radiomics modeland radiologist assessment were further compared by using theaccuracy (ACC), sensitivity and specificity. RESULTS: No clinical features showed statistically significant differences between true progression and pseudoprogression.The radiomic classifier demonstrated ACC, sensitivity, and specificity of 72.78%(95% confidence interval [CI]: 0.45,0.91), 78.36%(95%CI: 0.56,1.00) and 61.33%(95%CI: 0.20,0.82).The accuracy, sensitivity and specificity of three radiologists' assessment were66.23%(95% CI: 0.55,0.76), 61.50%(95% CI: 0.43,0.78) and 68.62%(95% CI: 0.55,0.80); 55.84%(95% CI: 0.45,0.66),69.25%(95% CI: 0.50,0.84) and 49.13%(95% CI: 0.36,0.62); 55.84%(95% CI: 0.45,0.66), 69.23%(95% CI: 0.50,0.84) and 47.06%(95% CI: 0.34,0.61), respectively. CONCLUSION: T1CE-based radiomics showed better classification performance compared with radiologists' assessment.The radiomics modelwas promising in differentiating pseudoprogression from true progression.

Better efficacy in differentiating WHO grade II from III oligodendrogliomas with machine-learning than radiologist's reading from conventional T1 contrast-enhanced and fluid attenuated inversion recovery images.

BACKGROUND: The medical imaging to differentiate World Health Organization (WHO) grade II (ODG2) from III (ODG3) oligodendrogliomas still remains a challenge. We investigated whether combination of machine leaning with radiomics from conventional T1 contrast-enhanced (T1 CE) and fluid attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI) offered superior efficacy. METHODS: Thirty-six patients with histologically confirmed ODGs underwent T1 CE and 33 of them underwent FLAIR MR examination before any intervention from January 2015 to July 2017 were retrospectively recruited in the current study. The volume of interest (VOI) covering the whole tumor enhancement were manually drawn on the T1 CE and FLAIR slice by slice using ITK-SNAP and a total of 1072 features were extracted from the VOI using 3-D slicer software. Random forest (RF) algorithm was applied to differentiate ODG2 from ODG3 and the efficacy was tested with 5-fold cross validation. The diagnostic efficacy of radiomics-based machine learning and radiologist's assessment were also compared. RESULTS: Nineteen ODG2 and 17 ODG3 were included in this study and ODG3 tended to present with prominent necrosis and nodular/ring-like enhancement (P < 0.05). The AUC, ACC, sensitivity, and specificity of radiomics were 0.798, 0.735, 0.672, 0.789 for T1 CE, 0.774, 0.689, 0.700, 0.683 for FLAIR, as well as 0.861, 0.781, 0.778, 0.783 for the combination, respectively. The AUCs of radiologists 1, 2 and 3 were 0.700, 0.687, and 0.714, respectively. The efficacy of machine learning based on radiomics was superior to the radiologists' assessment. CONCLUSIONS: Machine-learning based on radiomics of T1 CE and FLAIR offered superior efficacy to that of radiologists in differentiating ODG2 from ODG3.

Optimizing Texture Retrieving Model for Multimodal MR Image-Based Support Vector Machine for Classifying Glioma.

BACKGROUND: Accurate glioma grading plays an important role in patient treatment. PURPOSE: To investigate the influence of varied texture retrieving models on the efficacy of grading glioma with support vector machine (SVM). STUDY TYPE: Retrospective. POPULATION: In all, 117 glioma patients including 25, 29, and 63 grade II, III, and IV gliomas, respectively, based on WHO 2007. FIELD STRENGTH/SEQUENCE: 3.0T MRI/ T1 WI, T2 fluid-attenuated inversion recovery, contrast enhanced T1 , arterial spinal labeling, diffusion-weighted imaging (0, 30, 50, 100, 200, 300, 500, 800, 1000, 1500, 2000, 3000, and 3500 sec/mm2 ), and dynamic contrast-enhanced. ASSESSMENT: Texture attributes from 30 parametric maps were retrieved using four models, including Global, gray-level co-occurrence matrix (GLCM), gray-level run-length matrix (GLRLM), and gray-level size-zone matrix (GLSZM). Attributes derived from varied models were input into radial basis function SVM (RBF-SVM) combined with attribute selection using SVM-recursive feature elimination (SVM-RFE). The SVM model was trained and established with 80% randomly selected data of each category using 10-fold crossvalidation. The model performance was further tested using the remaining 20% data. STATISTICAL TESTS: Ten-fold crossvalidation was used to validate the model performance. RESULTS: Based on 30 parametric maps, 90, 240, 390, or 390 texture attributes were retrieved using the Global, GLCM, GLRLM, or GLSZM model, respectively. SVM-RFE was able to reduce attribute redundancy as well as improve RBF-SVM performance. Training data were oversampled by applying the Synthetic Minority Oversampling Technique (SMOTE) method to overcome the data imbalance problem; test results were able to further demonstrate the classifying performance of the final models. GLSZM using gray-level 64 was the optimal model to retrieve powerful image texture attributes to produce enough classifying power with an accuracy / area under the curve of 0.760/0.867 for the training and 0.875/0.971 for the independent test. Fifteen attributes were selected with SVM-RFE to provide comparable classifying efficacy. DATA CONCLUSION: When using image textures-based SVM classification of gliomas, the GLSZM model in combination with gray-level 64 and attribute selection may be an optimized solution. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:1263-1274.

Structural and advanced imaging in predicting MGMT promoter methylation of primary glioblastoma: a region of interest based analysis.

BACKGROUND: The methylation status of oxygen 6-methylguanine-DNA methyltransferase (MGMT) promoter has been associated with treatment response in glioblastoma(GBM). Using pre-operative MRI techniques to predict MGMT promoter methylation status remains inconclusive. In this study, we investigated the value of features from structural and advanced imagings in predicting the methylation of MGMT promoter in primary glioblastoma patients. METHODS: Ninety-two pathologically confirmed primary glioblastoma patients underwent preoperative structural MR imagings and the efficacy of structural image features were qualitatively analyzed using Fisher's exact test. In addition, 77 of the 92 patients underwent additional advanced MRI scans including diffusion-weighted (DWI) and 3-diminsional pseudo-continuous arterial spin labeling (3D pCASL) imaging. Apparent diffusion coefficient (ADC) and relative cerebral blood flow (rCBF) values within the manually drawn region-of-interest (ROI) were calculated and compared using independent sample t test for their efficacies in predicting MGMT promoter methylation. Receiver operating characteristic curve (ROC) analysis was used to investigate the predicting efficacy with the area under the curve (AUC) and cross validations. Multiple-variable logistic regression model was employed to evaluate the predicting performance of multiple variables. RESULTS: MGMT promoter methylation was associated with tumor location and necrosis (P <  0.05). Significantly increased ADC value (P <  0.001) and decreased rCBF (P <  0.001) were associated with MGMT promoter methylation in primary glioblastoma. The ADC achieved the better predicting efficacy than rCBF (ADC: AUC, 0.860; sensitivity, 81.1%; specificity, 82.5%; vs rCBF: AUC, 0.835; sensitivity, 75.0%; specificity, 78.4%; P = 0.032). The combination of tumor location, necrosis, ADC and rCBF resulted in the highest AUC of 0.914. CONCLUSION: ADC and rCBF are promising imaging biomarkers in clinical routine to predict the MGMT promoter methylation in primary glioblastoma patients.

Radiomics strategy for glioma grading using texture features from multiparametric MRI.

BACKGROUND: Accurate glioma grading plays an important role in the clinical management of patients and is also the basis of molecular stratification nowadays. PURPOSE/HYPOTHESIS: To verify the superiority of radiomics features extracted from multiparametric MRI to glioma grading and evaluate the grading potential of different MRI sequences or parametric maps. STUDY TYPE: Retrospective; radiomics. POPULATION: A total of 153 patients including 42, 33, and 78 patients with Grades II, III, and IV gliomas, respectively. FIELD STRENGTH/SEQUENCE: 3.0T MRI/T1 -weighted images before and after contrast-enhanced, T2 -weighted, multi-b-value diffusion-weighted and 3D arterial spin labeling images. ASSESSMENT: After multiparametric MRI preprocessing, high-throughput features were derived from patients' volumes of interests (VOIs). The support vector machine-based recursive feature elimination was adopted to find the optimal features for low-grade glioma (LGG) vs. high-grade glioma (HGG), and Grade III vs. IV glioma classification tasks. Then support vector machine (SVM) classifiers were established using the optimal features. The accuracy and area under the curve (AUC) was used to assess the grading efficiency. STATISTICAL TESTS: Student's t-test or a chi-square test were applied on different clinical characteristics to confirm whether intergroup significant differences exist. RESULTS: Patients' ages between LGG and HGG groups were significantly different (P < 0.01). For each patient, 420 texture and 90 histogram parameters were derived from 10 VOIs of multiparametric MRI. SVM models were established using 30 and 28 optimal features for classifying LGGs from HGGs and grades III from IV, respectively. The accuracies/AUCs were 96.8%/0.987 for classifying LGGs from HGGs, and 98.1%/0.992 for classifying grades III from IV, which were more promising than using histogram parameters or using the single sequence MRI. DATA CONCLUSION: Texture features were more effective for noninvasively grading gliomas than histogram parameters. The combined application of multiparametric MRI provided a higher grading efficiency. The proposed radiomic strategy could facilitate clinical decision-making for patients with varied glioma grades. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;48:1518-1528.

Combination of IVIM-DWI and 3D-ASL for differentiating true progression from pseudoprogression of Glioblastoma multiforme after concurrent chemoradiotherapy: study protocol of a prospective diagnostic trial.

BACKGROUND: Standard therapy for Glioblastoma multiforme (GBM) involves maximal safe tumor resection followed with radiotherapy and concurrent adjuvant temozolomide. About 20 to 30% patients undergoing their first post-radiation MRI show increased contrast enhancement which eventually recovers without any new treatment. This phenomenon is referred to as pseudoprogression. Differentiating tumor progression from pseudoprogression is critical for determining tumor treatment, yet this capacity remains a challenge for conventional magnetic resonance imaging (MRI). Thus, a prospective diagnostic trial has been established that utilizes multimodal MRI techniques to detect tumor progression at its early stage. The purpose of this trial is to explore the potential role of intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) and three-dimensional arterial spin labeling imaging (3D-ASL) in differentiating true progression from pseudoprogression of GBM. In addition, the diagnostic performance of quantitative parameters obtained from IVIM-DWI and 3D-ASL, including apparent diffusion coefficient (ADC), slow diffusion coefficient (D), fast diffusion coefficient (D*), perfusion fraction (f), and cerebral blood flow (CBF), will be evaluated. METHODS: Patients that recently received a histopathological diagnosis of GBM at our hospital are eligible for enrollment. The patients selected will receive standard concurrent chemoradiotherapy and adjuvant temozolomide after surgery, and then will undergo conventional MRI, IVIM-DWI, 3D-ASL, and contrast-enhanced MRI. The quantitative parameters, ADC, D, D*, f, and CBF, will be estimated for newly developed enhanced lesions. Further comparisons will be made with unpaired t-tests to evaluate parameter performance in differentiating true progression from pseudoprogression, while receiver-operating characteristic (ROC) analyses will determine the optimal thresholds, as well as sensitivity and specificity. Finally, relationships between these parameters will be assessed with Pearson's correlation and partial correlation analyses. DISCUSSION: The results of this study may demonstrate the potential value of using multimodal MRI techniques to differentiate true progression from pseudoprogression in its early stages to help decision making in early intervention and improve the prognosis of GBM. TRIAL REGISTRATION: This study has been registered at ClinicalTrials.gov ( NCT02622620 ) on November 18, 2015 and published on March 28, 2016.

Disrupted topological properties of structural brain networks present a glutamatergic neuropathophysiology in people with narcolepsy.

STUDY OBJECTIVES: Growing evidences have documented various abnormalities of the white matter bundles in people with narcolepsy. We sought to evaluate topological properties of brain structural networks, and their association with symptoms and neuropathophysiological features in people with narcolepsy. METHODS: Diffusion tensor imaging was conducted for people with narcolepsy (n = 30) and matched healthy controls as well as symptoms assessment. Structural connectivity for each participant was generated to analyze global and regional topological properties and their correlations with narcoleptic features. Further human brain transcriptome was extracted and spatially registered for connectivity vulnerability. Genetic functional enrichment analysis was performed and further clarified using in vivo emission computed tomography data. RESULTS: A wide and dramatic decrease in structural connectivities was observed in people with narcolepsy, with descending network degree and global efficiency. These metrics were not only correlated with sleep latency and awakening features, but also reflected alterations of sleep macrostructure in people with narcolepsy. Network-based statistics identified a small hyperenhanced subnetwork of cingulate gyrus that was closely related to rapid eye movement sleep behavior disorder (RBD) in narcolepsy. Further imaging genetics analysis suggested glutamatergic signatures were responsible for the preferential vulnerability of connectivity alterations in people with narcolepsy, while additional PET/SPECT data verified that structural alteration was significantly correlated with metabotropic glutamate receptor 5 (mGlutR5) and N-methyl-D-aspartate receptor (NMDA). CONCLUSIONS: People with narcolepsy endured a remarkable decrease in the structural architecture, which was not only closely related to narcolepsy symptoms but also glutamatergic signatures.

Quantifying the regime of thermodynamic control for solid-state reactions during ternary metal oxide synthesis.

The success of solid-state synthesis often hinges on the first intermediate phase that forms, which determines the remaining driving force to produce the desired target material. Recent work suggests that when reaction energies are large, thermodynamics primarily dictates the initial product formed, regardless of reactant stoichiometry. Here, we validate this principle and quantify its constraints by performing in situ characterization on 37 pairs of reactants. These experiments reveal a threshold for thermodynamic control in solid-state reactions, whereby initial product formation can be predicted when its driving force exceeds that of all other competing phases by ≥60 milli-electron volt per atom. In contrast, when multiple phases have a comparable driving force to form, the initial product is more often determined by kinetic factors. Analysis of the Materials Project data shows that 15% of possible reactions fall within the regime of thermodynamic control, highlighting the opportunity to predict synthesis pathways from first principles.

Surface band bending caused by native oxides on solution-processed twinned InSb nanowires with p-type conductivity.

Indium antimonide nanowires (InSb NWs) are attractive building-block candidates for bottom-up construction of high-efficiency electronics and optoelectronics due to their narrow direct band gap, fast room temperature carrier mobilities and small exciton binding energy. However, InSb NWs synthesized by the vapor-liquid-solution (VLS) mechanism generally suffer from an increased carrier and phonon scattering rate, which is thought to be caused by randomly distributed crystal defects along the NW growth direction. In this study, by utilizing the recently developed low-temperature, solution-processed technique, these crystal defects were successfully suppressed by periodically distributed twin planes to form twinned InSb nanowires. Importantly, measurements of the electrical transport properties of field effect transistors (FETs) reveal that the InSb NWs exhibit a hole-dominated conductivity with room temperature mobilities of up to 50.71 cm2 V-1 s-1, which is distinctly contrary to the intrinsic n-type InSb NWs. This observation of n-p switching behavior is probably attributed to the surface band bending effect with regard to the Fermi energy level, which is caused by surface oxide trap states arising from the native-oxide layer at the surface of the InSb NWs. All these results illustrate that the as-prepared colloidal InSb NWs can potentially be used as p-type materials for integration with next-generation nanoscale electronics and optoelectronics via surface engineering.

Role of CD34 in inflammatory bowel disease.

Inflammatory bowel disease (IBD) is caused by a variety of pathogenic factors, including chronic recurrent inflammation of the ileum, rectum, and colon. Immune cells and adhesion molecules play an important role in the course of the disease, which is actually an autoimmune disease. During IBD, CD34 is involved in mediating the migration of a variety of immune cells (neutrophils, eosinophils, and mast cells) to the inflammatory site, and its interaction with various adhesion molecules is involved in the occurrence and development of IBD. Although the function of CD34 as a partial cell marker is well known, little is known on its role in IBD. Therefore, this article describes the structure and biological function of CD34, as well as on its potential mechanism in the development of IBD.

Unraveling Li growth kinetics in solid electrolytes due to electron beam charging.

Revealing the local structure of solid electrolytes (SEs) with electron microscopy is critical for the fundamental understanding of the performance of solid-state batteries (SSBs). However, the intrinsic structural information in the SSB can be misleading if the sample's interactions with the electron beams are not fully understood. In this work, we systematically investigate the effect of electron beams on Al-doped lithium lanthanum zirconium oxide (LLZO) under different imaging conditions. Li metal is observed to grow directly on the clean surface of LLZO. The Li metal growth kinetics and the morphology obtained are found to be heavily influenced by the temperature, accelerating voltage, and electron beam intensity. We prove that the lithium growth is due to the LLZO delithiation activated by a positive charging effect under electron beam emission. Our results deepen the understanding of the electron beam impact on SEs and provide guidance for battery material characterization using electron microscopy.

Dataset of solution-based inorganic materials synthesis procedures extracted from the scientific literature.

The development of a materials synthesis route is usually based on heuristics and experience. A possible new approach would be to apply data-driven approaches to learn the patterns of synthesis from past experience and use them to predict the syntheses of novel materials. However, this route is impeded by the lack of a large-scale database of synthesis formulations. In this work, we applied advanced machine learning and natural language processing techniques to construct a dataset of 35,675 solution-based synthesis procedures extracted from the scientific literature. Each procedure contains essential synthesis information including the precursors and target materials, their quantities, and the synthesis actions and corresponding attributes. Every procedure is also augmented with the reaction formula. Through this work, we are making freely available the first large dataset of solution-based inorganic materials synthesis procedures.