Highly selective detection of deoxyribonucleic acid in living cells using RecA-green fluorescent protein-single-stranded deoxyribonucleic acid filament fluorescence resonance energy transfer probe.

A fluorescence resonance energy transfer (FRET) method was developed for double-stranded deoxyribonucleic acid (dsDNA) detection in living cells using the RecA-GFP (green fluorescent protein) fusion protein filament. In brief, the thiol-modified single-stranded DNA (ssDNA) was attached to gold nanoparticles (AuNPs); on the contrary, the prepared RecA-GFP fusion protein interacted with ssDNA. Due to the FRET between AuNPs and RecA-GFP, fluorescence of RecA-GFP fusion protein was quenched. In the presence of homologous dsDNA, homologous recombination occurred to release RecA-GFP fusion protein. Thus, the fluorescence of RecA-GFP was recovered. The dsDNA concentration was detected using fluorescence intensity of RecA-GFP. Under optimal conditions, this method could detect dsDNA activity as low as 0.015 optical density (OD) Escherichia coli cells, with a wide linear range from 0.05 to 0.9 OD cells, and the regression equation was ΔF = 342.7c + 78.9, with a linear relationship coefficient of 0.9920. Therefore, it provided a promising approach for the selective detection of dsDNA in living cells for early clinical diagnosis of genetic diseases.

RFEM: A framework for essential microRNA identification in mice based on rotation forest and multiple feature fusion.

With the increasing number of microRNAs (miRNAs), identifying essential miRNAs has become an important task that needs to be solved urgently. However, there are few computational methods for essential miRNA identification. Here, we proposed a novel framework called Rotation Forest for Essential MicroRNA identification (RFEM) to predict the essentiality of miRNAs in mice. We first constructed 1,264 miRNA features of all miRNA samples by fusing 38 miRNA features obtained from the PESM paper and 1,226 miRNA functional features calculated based on miRNA-target gene interactions. Then, we employed 182 training samples with 1,264 features to train the rotation forest model, which was applied to compute the essentiality scores of the candidate samples. The main innovations of RFEM were as follows: 1) miRNA functional features were introduced to enrich the diversity of miRNA features; 2) the rotation forest model used decision tree as the base classifier and could increase the difference among base classifiers through feature transformation to achieve better ensemble results. Experimental results show that RFEM significantly outperformed two previous models with the AUC (AUPR) of 0.942 (0.944) in three comparison experiments under 5-fold cross validation, which proved the model's reliable performance. Moreover, ablation study was further conducted to demonstrate the effectiveness of the novel miRNA functional features. Additionally, in the case studies of assessing the essentiality of unlabeled miRNAs, experimental literature confirmed that 7 of the top 10 predicted miRNAs have crucial biological functions in mice. Therefore, RFEM would be a reliable tool for identifying essential miRNAs.

ß-Lapachone induces ferroptosis of colorectal cancer cells via NCOA4-mediated ferritinophagy by activating JNK pathway.

ß-Lapachone is a natural product that can promote ROS generation and ultimately triggers tumor cells death by inducing DNA damage. Recent studies have indicated that the targeting of ferroptosis or iron metabolism is a feasible strategy for treating cancer. In this study, bulk RNA-seq analysis suggested that ß-Lapachone might induce ferroptosis in CRC cells. We further tested this hypothesis using a xenograft model of human colorectal cancer as an animal model and in SW620 and DLD-1 of CRC cell lines. Western blot was used to determine the key proteins of ferroptosis (SLC7A11, GPX4), autophagy (LC3B, P62, ATG7), ferritinophagy (NCOA4, FTH1, TFRC), and JNK pathway (p-JNK, JNK, p-c-Jun, c-Jun). The levels of MDA, GSH/GSSG, lipid ROS, and intracellular ferrous iron were determined after ß-Lapachone treatment, and inhibitors of various pathways, including NAC, Ferrostatin-1, DFO, 3-MA, and SP600125 were utilized to explore the molecular mechanism underlying ß-Lapachone-mediated ferroptosis. As the result, we identified that ß-Lapachone inhibited cell proliferation and induced apoptosis, autophagy, and ROS generation. In addition, ß-Lapachone induced ferroptosis as demonstrated by intra-cellular iron overload, increased levels of lipid ROS and MDA. Mechanistically, JNK signaling pathway was involved in ß-Lapachone-induced xCT/GPX4-mediated ferroptosis and NCOA4-mediated ferritinophagy in CRC cells. In vivo experiments in nude mice demonstrated that ß-Lapachone significantly inhibited CRC growth and induced ferroptosis and NCOA4-mediated ferritinophagy. These findings not only identify a novel role for ß-Lapachone in ferroptosis but also indicate that ß-Lapachone may be a valuable candidate for the research and development of anti-cancer therapeutic agents.

Identifying new players in structural synaptic plasticity through dArc1 interrogation.

The formation, expansion, and pruning of synapses, known as structural synaptic plasticity, is needed for learning and memory, and perturbation of plasticity is associated with many neurological disorders and diseases. Previously, we observed that the Drosophila homolog of Activity-regulated cytoskeleton-associated protein (dArc1), forms a capsid-like structure, associates with its own mRNA, and is transported across synapses. We demonstrated that this transfer is needed for structural synaptic plasticity. To identify mRNAs that are modified by dArc1 in presynaptic neuron and postsynaptic muscle, we disrupted the expression of dArc1 and performed genomic analysis with deep sequencing. We found that dArc1 affects the expression of genes involved in metabolism, phagocytosis, and RNA-splicing. Through immunoprecipitation we also identified potential mRNA cargos of dArc1 capsids. This study suggests that dArc1 acts as a master regulator of plasticity by affecting several distinct and highly conserved cellular processes.

Paper test strip for fluorescence detection of iron ion based on nitrogen, zinc and copper codoped carbon dots.

In this study, a test strip for fluorometric analysis of iron ion (Fe3+) was constructed based on nitrogen, zinc and copper codoped carbon dots (NZC-CDs) as fluorescence probes. NZC-CDs were synthesized by hydrothermal method. The morphology, size, components, crystal state and optical properties of NZC-CDs were characterized by transmission electron microscope (TEM), Fourier-transform infrared (FT-IR), x-ray photoelectron spectroscopy (XPS), x-ray diffraction (XRD), UV-vis absorption and fluorescence spectroscopy techniques, respectively. NZC-CDs exhibited bright blue fluorescence under UV lamp with a quantum yield at 17.76%. The fluorescence of NZC-CDs was quenched by Fe3+possibly due to the static quenching. The possible fluorescence quenching mechanism was also discussed. The quenching fluorescence was linear with the concentration of Fe3+in the range of 2.5-400µM with a low detection limit of 0.5µM. For the convenient detection, the test strips based on filter paper were employed for Fe3+assay. Moreover, the present approach was successfully applied in the determination of Fe3+in real samples including black fungus, duck blood and pork liver. The sensing method had the potential application in more food analysis.

Analysis of inter-joint coordination during the sit-to-stand and stand-to-sit tasks in stroke patients with hemiplegia.

BACKGROUND: Inter-joint coordination is an important factor affecting postural stability, and its variability increases after fatigue. This study aimed to investigate the coordination pattern of lower limb joints during the sit-to-stand (Si-St) and stand-to-sit (St-Si) tasks in stroke patients and explore the influence of duration on inter-joint coordination. METHODS: Thirteen stroke hemiplegia patients (five with left paretic and eight right paretic) and thirteen age-matched healthy subjects were recruited. The Si-St and St-Si tasks were performed while each subject's joint kinematics were recorded using a three-dimensional motion capture system. Sagittal joint angles of the bilateral hip, knee and ankle joints as well as the movement duration were extracted. The angle-angle diagrams for the hip-knee, hip-ankle and knee-ankle joint were plotted to assess the inter-joint coordination. The inter-joint coordination was quantified using geometric characteristics of the angle-angle diagrams, including perimeter, area and dimensionless ratio. The coefficient of variation (CV) was performed to compare variability of the coordination parameters. RESULTS: There were no significant differences in the perimeter, area and dimensionless ratio values of the bilateral hip-knee, hip-ankle and knee-ankle inter-joints during Si-St and St-Si tasks in the stroke group. The perimeter values of bilateral hip-knee and knee-ankle inter-joints in the stroke group were lower (P<0.05) than in the healthy group during Si-St and St-Si tasks. Although no significant bilateral differences were found, the inter-joint coordination in stroke patients decreased with the increased movement duration of both Si-St and St-Si tasks. Additionally, the CV of the hip-knee inter-joint area during the Si-St task in the stroke group was less than (P<0.05) that in the healthy group. CONCLUSION: Stroke patients exhibit different inter-joint coordination patterns than healthy controls during the Si-St and St-Si tasks. The duration affects joint coordination, and inter-joint coordination is limited on the hemiplegic side joint pairs, which may lead to inconsistency in the rhythm of the left and right leg inter-joint movements and increase the risk of falls. These findings provide new insights into motor control rehabilitation strategies and may help planning targeted interventions for stoke patients with hemiplegia.

High accuracy epidermal growth factor receptor mutation prediction via histopathological deep learning.

BACKGROUND: The detection of epidermal growth factor receptor (EGFR) mutations in patients with non-small cell lung cancer is critical for tyrosine kinase inhibitor therapy. EGFR detection requires tissue samples, which are difficult to obtain in some patients, costing them the opportunity for further treatment. To realize EGFR mutation prediction without molecular detection, we aimed to build a high-accuracy deep learning model with only haematoxylin and eosin (H&E)-stained slides. METHODS: We collected 326 H&E-stained non-small cell lung cancer slides from Beijing Chest Hospital, China, and used 226 slides (88 with EGFR mutations) for model training. The remaining 100 images (50 with EGFR mutations) were used for testing. We trained a convolutional neural network based on ResNet-50 to classify EGFR mutation status on the slide level. RESULTS: The sensitivity and specificity of the model were 76% and 74%, respectively, with an area under the curve of 0.82. When applying the double-threshold approach, 33% of the patients could be predicted by the deep learning model as EGFR positive or negative with a sensitivity and specificity of 100.0% and 87.5%. The remaining 67% of the patients got an uncertain result and will be recommenced to perform further examination. By incorporating adenocarcinoma subtype information, we achieved 100% sensitivity in predicting EGFR mutations in 37.3% of adenocarcinoma patients. CONCLUSIONS: Our study demonstrates the potential of a deep learning-based EGFR mutation prediction model for rapid and cost-effective pre-screening. It could serve as a high-accuracy complement to current molecular detection methods and provide treatment opportunities for non-small cell lung cancer patients from whom limited samples are available.

Deep learning-based diagnosis of histopathological patterns for invasive non-mucinous lung adenocarcinoma using semantic segmentation.

OBJECTIVES: The application of artificial intelligence (AI) to the field of pathology has facilitated the development of digital pathology, hence, making AI-assisted diagnosis possible. Due to the variety of lung cancers and the subjectivity of manual evaluation, invasive non-mucinous lung adenocarcinoma (ADC) is difficult to diagnose. We aim to offer a deep learning solution that automatically classifies invasive non-mucinous lung ADC histological subtypes. DESIGN: For this investigation, 523 whole-slide images (WSIs) were obtained. We divided 376 of the WSIs at random for model training. According to WHO diagnostic criteria, six histological components of invasive non-mucinous lung ADC, comprising lepidic, papillary, acinar, solid, micropapillary and cribriform arrangements, were annotated at the pixel level and employed as the predicting target. We constructed the deep learning model using DeepLab v3, and used 27 WSIs for model validation and the remaining 120 WSIs for testing. The predictions were analysed by senior pathologists. RESULTS: The model could accurately predict the predominant subtype and the majority of minor subtypes and has achieved good performance. Except for acinar, the area under the curve of the model was larger than 0.8 for all the subtypes. Meanwhile, the model was able to generate pathological reports. The NDCG scores were greater than 75%. Through the analysis of feature maps and incidents of model misdiagnosis, we discovered that the deep learning model was consistent with the thought process of pathologists and revealed better performance in recognising minor lesions. CONCLUSIONS: The findings of the deep learning model for predicting the major and minor subtypes of invasive non-mucinous lung ADC are favourable. Its appearance and sensitivity to tiny lesions can be of great assistance to pathologists.

Cooperative Boron and Vanadium Doping of Nickel Phosphides for Hydrogen Evolution in Alkaline and Anion Exchange Membrane Water/Seawater Electrolyzers.

Developing low-cost and high-performance transition metal-based electrocatalysts is crucial for realizing sustainable hydrogen evolution reaction (HER) in alkaline media. Here, a cooperative boron and vanadium co-doped nickel phosphide electrode (B, V-Ni2 P) is developed to regulate the intrinsic electronic configuration of Ni2 P and promote HER processes. Experimental and theoretical results reveal that V dopants in B, V-Ni2 P greatly facilitate the dissociation of water, and the synergistic effect of B and V dopants promotes the subsequent desorption of the adsorbed hydrogen intermediates. Benefiting from the cooperativity of both dopants, the B, V-Ni2 P electrocatalyst requires a low overpotential of 148 mV to attain a current density of -100 mA cm-2  with excellent durability. The B, V-Ni2 P is applied as the cathode in both alkaline water electrolyzers (AWEs) and anion exchange membrane water electrolyzers (AEMWEs). Remarkably, the AEMWE delivers a stable performance to achieve 500 and 1000 mA cm-2  current densities at a cell voltage of 1.78 and 1.92 V, respectively. Furthermore, the developed AWEs and AEMWEs also demonstrate excellent performance for overall seawater electrolysis.

Tuning Piezoelectricity via Thermal Annealing at a Freestanding Ferroelectric Membrane.

Tuning the ferroelectric domain structure by a combination of elastic and electrostatic engineering provides an effective route for enhanced piezoelectricity. However, for epitaxial thin films, the clamping effect imposed by the substrate does not allow aftergrowth tuning and also limits the electromechanical response. In contrast, freestanding membranes, which are free of substrate constraints, enable the tuning of a subtle balance between elastic and electrostatic energies, giving new platforms for enhanced and tunable functionalities. Here, highly tunable piezoelectricity is demonstrated in freestanding PbTiO3 membranes, by varying the ferroelectric domain structures from c-dominated to c/a and a domains via aftergrowth thermal treatment. Significantly, the piezoelectric coefficient of the c/a domain structure is enhanced by a factor of 2.5 compared with typical c domain PbTiO3. This work presents a new strategy to manipulate the piezoelectricity in ferroelectric membranes, highlighting their great potential for nano actuators, transducers, sensors and other NEMS device applications.

RSL1D1 knockdown induces ferroptosis and mediates ferrous iron accumulation in senescent cells by inhibiting FTH1 mRNA stability.

Iron metabolism plays an important role in maintaining cellular multiple biological functions. Dysfunction of iron homeostasis-maintaining systems was observed in many diseases, including cancer. Ribosomal L1 domain-containing 1 (RSL1D1) is an RNA-binding protein involved in multiple cellular processes, including cellular senescence, proliferation and apoptosis. However, the regulatory mechanism of RSL1D1 underlying cellular senescence and its biological process in colorectal cancer (CRC) is not clearly understood. Here, we report that RSL1D1 expression is downregulated by ubiquitin-mediated proteolysis in senescence-like CRC cells. RSL1D1, as an anti-senescence factor, is frequently upregulated in CRC, and elevated RSL1D1 prevents CRC cells from senescence-like phenotype, and correlated with poor prognosis of CRC patients. Knockdown of RSL1D1 inhibited cell proliferation, and induced cell cycle arrest and apoptosis. Notably, RSL1D1 plays important roles in regulating iron metabolism of cancer cells. In RSL1D1-knockdown cells, FTH1 expression was significantly decreased, while transferrin receptor 1 expression was increased, leading to intracellular ferrous iron accumulation, which subsequently promoted ferroptosis, indicated by the increased malondialdehyde and decreased GPX4 levels. Mechanically, RSL1D1 directly bounds with 3' untranslated region of FTH1 and subsequently promoted the mRNA stability. Moreover, RSL1D1-mediated downregulation of FTH1 was also observed in H2O2-induced senescence-like cancer cells. Taken together, these findings support RSL1D1 plays an important role in regulating intracellular iron homeostasis in CRC, and suggest that RSL1D1 could be a potential therapeutic target for cancer treatment.

Cotton swabs decorated with Ag@BPQD for the fluorescence determination of 3-aminosalicylic and 5-aminosalicylic acid.

Novel and portable cotton swab-based fluorometry was constructed for the first time for 3-aminosalicylic acid (3-ASA) and 5-aminosalicylic acid (5-ASA) detection. It was carried out by fluorescence enhancement on silver (Ag)-doped black phosphorus quantum dots (Ag@BPQD). Ag@BPQD were prepared from AgNO3 and bulk black phosphorus in N, N-dimethylformamide (DMF) solution by solvothermal decomposition after mechanical exfoliation. Ag@BPQD show blue fluorescence with a quantum yield (QY) of 2.43%. In the presence of Ag@BPQD, 3-ASA exhibited bright blue fluorescence (λex = 328 nm, λem = 448 nm). The fluorescence of 5-ASA was also enhanced significantly and exhibited bright green emission (λex = 328 nm, λem = 484 nm). The linear range of 3-ASA is 0-90 µM with a detection limit (LOD) of 0.10 µM, relative standard deviation (RSD) ≤ 2.04%, and a recovery range of 98.0-104.3%. The linear range of 5-ASA is 0-120 µM with a LOD of 0.12 µM, RSD ≤ 1.34%, and a recovery range of 98.0-101.3%. When 3-ASA and 5-ASA were mixed in different ratios, the fluorescence showed different colors. The possible mechanism of the interaction between 3-ASA (or 5-ASA) and Ag@BPQD may be ascribed to the generation of excited-state intramolecular proton transfer. To realize convenient detection of 3-ASA and 5-ASA, a Ag@BPQD portable sensing method using cotton swabs were built. The proposed approach provides the detection of 3-ASA and 5-ASA in environmental and biological samples with high efficiency, accuracy and portability.

Assessment of automatic rib fracture detection on chest CT using a deep learning algorithm.

OBJECTIVES: To evaluate deep neural networks for automatic rib fracture detection on thoracic CT scans and to compare its performance with that of attending-level radiologists using a large amount of datasets from multiple medical institutions. METHODS: In this retrospective study, an internal dataset of 12,208 emergency room (ER) trauma patients and an external dataset of 1613 ER trauma patients taking chest CT scans were recruited. Two cascaded deep neural networks based on an extended U-Net architecture were developed to segment ribs and detect rib fractures respectively. Model performance was evaluated with a 95% confidence interval (CI) on both the internal and external dataset, and compared with attending-level radiologist readings using t test. RESULTS: On the internal dataset, the AUC of the model for detecting fractures at per-rib level was 0.970 (95% CI: 0.968, 0.972) with sensitivity of 93.3% (95% CI: 92.0%, 94.4%) at a specificity of 98.4% (95% CI: 98.3%, 98.5%). On the external dataset, the model obtained an AUC of 0.943 (95% CI: 0.941, 0.945) with sensitivity of 86.2% (95% CI: 85.0%, 87.3%) at a specificity of 98.8% (95% CI: 98.7%, 98.9%), compared to the sensitivity of 70.5% (95% CI: 69.3%, 71.8%) (p < .0001) and specificity of 98.8% (95% CI: 98.7%, 98.9%) (p = 0.175) by attending radiologists. CONCLUSIONS: The proposed DL model is a feasible approach to identify rib fractures on chest CT scans, at the very least, reaching a level on par with attending-level radiologists. KEY POINTS: • Deep learning-based algorithms automatically detected rib fractures with high sensitivity and reasonable specificity on chest CT scans. • The performance of deep learning-based algorithms reached comparable diagnostic measures with attending level radiologists for rib fracture detection on chest CT scans. • The deep learning models, similar to human readers, were susceptible to the inconspicuity and ambiguity of target lesions. More training data was required for subtle lesions to achieve comparable detection performance.

Rapid Screening of Phenolic Compounds with Anti-Enteritis Activity from Camellia oleifera Oil Using a Smurf Drosophila Model and Molecular Docking Methods.

Screening and identifying the active compounds in foods are important for the development and utilization of functional foods. In this study, the anti-enteritis activity of ethanol extract from Camellia oleifera oil (PECS) was quickly evaluated using a Smurf Drosophila model and the metabolomics approach, combined with molecular docking techniques, were performed to rapidly screen and identify compounds with potential anti-enteritis activity in PECS. PECS showed good anti-enteritis activity and inhibited the activity of 5-lipoxygenase (LOX), cyclooxygenase 2 (COX-2) and inducible nitric oxide synthase (iNOS). In particular, wighteone and p-octopamine were newly identified in C. oleifera oil and were proven to have good anti-enteritis activity. The inhibitory activity of kaempferitrin (IC50 = 0.365 mmol L-1) was higher than that of wighteone (IC50 = 0.424 mmol L-1) and p-octopamine (IC50 = 0.402 mmol L-1). Of note, the IC50 value of salazosulfapyridine was 0.810 mmol L-1. Inhibition of LOX activity is likely one of the anti-enteritis mechanisms of PECS. These new findings lay the foundation for further investigations into the underlying mechanisms of anti-enteritis activity in C. oleifera oil.

Clinical-grade endometrial cancer detection system via whole-slide images using deep learning.

The accurate pathological diagnosis of endometrial cancer (EC) improves the curative effect and reduces the mortality rate. Deep learning has demonstrated expert-level performance in pathological diagnosis of a variety of organ systems using whole-slide images (WSIs). It is urgent to build the deep learning system for endometrial cancer detection using WSIs. The deep learning model was trained and validated using a dataset of 601 WSIs from PUPH. The model performance was tested on three independent datasets containing a total of 1,190 WSIs. For the retrospective test, we evaluated the model performance on 581 WSIs from PUPH. In the prospective study, 317 consecutive WSIs from PUPH were collected from April 2022 to May 2022. To further evaluate the generalizability of the model, 292 WSIs were gathered from PLAHG as part of the external test set. The predictions were thoroughly analyzed by expert pathologists. The model achieved an area under the receiver operating characteristic curve (AUC), sensitivity, and specificity of 0.928, 0.924, and 0.801, respectively, on 1,190 WSIs in classifying EC and non-EC. On the retrospective dataset from PUPH/PLAGH, the model achieved an AUC, sensitivity, and specificity of 0.948/0.971, 0.928/0.947, and 0.80/0.938, respectively. On the prospective dataset, the AUC, sensitivity, and specificity were, in order, 0.933, 0.934, and 0.837. Falsely predicted results were analyzed to further improve the pathologists' confidence in the model. The deep learning model achieved a high degree of accuracy in identifying EC using WSIs. By pre-screening the suspicious EC regions, it would serve as an assisted diagnostic tool to improve working efficiency for pathologists.

Unlocking the in situ Li plating dynamics and evolution mediated by diverse metallic substrates in all-solid-state batteries.

The mechanisms of Li deposition behaviors, which overwhelmingly affect battery performances and safety, are far to be understood in solid-state batteries. Here, using in situ micro-nano electrochemical scanning electron microscopy (SEM) manipulation platform, dynamic Li plating behaviors on 10 metallic substrates have been tracked, and the underlying mechanisms for dendrite-free Li plating are elucidated. Distinct Li deposition behaviors on Cu, Ti, Ni, Bi, Cr, In, Ag, Au, Pd, and Al are revealed quantitatively in nucleation densities, growth rates, and anisotropic ratios. For Li alloyable metals, the dynamic Li alloying process before Li growth is visually captured. It is concluded that a good affinity for Li and appropriate lattice compatibility between the substrate and Li are needed to facilitate homogeneous Li plating. Our work not only uncovers the Li plating dynamics, shedding light on the design of solid-state batteries, but also provides a powerful integrated SEM platform for future in-depth investigation of solid-state batteries.

Inner-filter Effect Induced Fluorescence Quenching of Carbon Dots for Cr(VI) Detection with High Sensitivity.

Carbon dots (CDs) were used to develop a sensitive sensing technique for detecting Cr(VI). CDs were made using a hydrothermal technique from citric acid and glutamic acid. These prepared CDs emitted blue fluorescence under excitation of 350 nm (λem = 420 nm), and the fluorescence quantum yield was 48.41%. Transmission electron microscope was used to examine the morphology of the CDs, which had an average size of 2.21 ± 0.39 nm. The elementary composition and bonding structure of the CDs were conducted by XPS and FT-IR spectrum. Cr(VI) quenched the fluorescence of CDs through a static quenching effect and an inner filter effect, allowing Cr(VI) to be detected quantitatively. This approach was used to detect Cr(VI) in two samples of water, with the findings demonstrating that it is reliable and accurate. The fluorescence intensity change was linearly related to the concentration of Cr(VI) in the range from 0.5 to 400 µM, with the detection limit being 0.10 µM. This approach has the virtues of wide detection range, low cost and fast response. The strategy has a great application prospect for detecting Cr(VI) in practical samples.

Stabilizing the interface of PEO solid electrolyte to lithium metal anode via a g-C3N4 mediator.

g-C3N4 is introduced to the PEO electrolyte as a mediator to stabilize the interface to lithium metal anode. As a result, the interface resistance is stabilized after cycling and the symmetric cell exhibits a cycle life over 900 h, indicating that the interface stability is evidently promoted.

Heterostructured V-Doped Ni2 P/Ni12 P5 Electrocatalysts for Hydrogen Evolution in Anion Exchange Membrane Water Electrolyzers.

Regulating the electronic structure and intrinsic activity of catalysts' active sites with optimal hydrogen intermediates adsorption is crucial to enhancing the hydrogen evolution reaction (HER) in alkaline media. Herein, a heterostructured V-doped Ni2 P/Ni12 P5 (V-Ni2 P/Ni12 P5 ) electrocatalyst is  fabricated through a hydrothermal treatment and controllable phosphidation process. In comparison with pure-phase V-Ni2 P, in/ex situ characterizations and theoretical calculations reveal a redistribution of electrons and active sites in V-Ni2 P/Ni12 P5 due to the V doping and heterointerfaces effect. The strong coupling between Ni2 P and Ni12 P5 at the interface leads to an increased electron density at interfacial Ni sites while depleting at P sites, with V-doping further promoting the electron accumulation at Ni sites. This is accompanied by the change of active sites from the anionic P sites to the interfacial Ni-V bridge sites in V-Ni2 P/Ni12 P5 . Benefiting from the interface electronic structure, increased number of active sites, and optimized H-adsorption energy, the V-Ni2 P/Ni12 P5 exhibits an overpotential of 62 mV to deliver 10 mA cm-2 and excellent long-term stability for HER. The V-Ni2 P/Ni12 P5 catalyst is applied for anion exchange membrane water electrolysis to deliver superior performance with a current density of 500 mA cm-2 at a cell voltage of 1.79 V and excellent durability.

Stabilizing the Halide Solid Electrolyte to Lithium by a ß-Li3N Interfacial Layer.

As a new class of solid electrolytes, halide solid electrolytes have the advantages of high ionic conductivity at room temperature, stability to high-voltage cathodes, and good deformability, but they generally show a problem of being unstable to a lithium anode. Here, we report the use of Li3N as an interface modification layer to improve the interfacial stability of Li2ZrCl6 to the Li anode. We found that commercial Li3N can be easily transformed into an α-phase and a ß-phase by ball-milling and annealing, respectively, in which ß-phase Li3N simultaneously has high room-temperature ionic conductivity and good stability to both Li and Li2ZrCl6, making it a good choice for an artificial interface layer material. After the modification of the ß-Li3N interfacial layer, the interfacial impedance between Li2ZrCl6 and the Li anode decreased from 1929 to ∼400 Ω. At a current density of 0.1 mA cm-2, the overpotential of the Li symmetric cell decreased from 250 to ∼50 mV, which did not show an obvious increase for at least 300 h, indicating that the ß-Li3N interface layer effectively improves the interfacial stability between Li2ZrCl6 and Li.