In response to the call for safer high‐energy‐density storage systems, high‐voltage solid‐state Li metal batteries have attracted extensive attention. Therefore, solid electrolytes are required to be ...stable against both Li anode and high‐voltage cathodes; nevertheless, the requirements still cannot be completely satisfied. Herein, a heterogeneous multilayered solid electrolyte (HMSE) is proposed to broaden electrochemical window of solid electrolytes to 0–5 V, through different electrode/electrolyte interfaces to overcome the interfacial instability problems. Oxidation‐resistance poly(acrylonitrile) (PAN) is in contact with the cathode, while reduction tolerant polyethylene glycol diacrylate contacts with Li metal anode. A Janus and flexible PAN@Li1.4Al0.4Ge1.6(PO4)3 (80 wt%) composite electrolyte is designed as intermediate layer to inhibit dendrite penetration and ensure compact interface. Paired with LiNi0.6Co0.2Mn0.2O2 and LiNi0.8Co0.1Mn0.1O2 cathodes, which are rarely used in solid‐state batteries, the solid‐state Li metal batteries with HMSE exhibit excellent electrochemical performance including high capacity and long cycle life. Besides, the Li||Li symmetric batteries maintain a stable polarization less than 40 mV for more than 1000 h under 2 mA cm−2 and effective inhibition of dendrite formation. This study offers a promising approach to extend the applications of solid electrolytes for high‐voltage solid‐state Li metal batteries.
A heterogeneous multilayered structure that expands the electrochemical window of solid electrolytes is designed. The oxidation‐resistant poly(acrylonitrile) (PAN) and reduction‐tolerant polyethylene glycol diacrylate integrated with the Janus and flexible PAN@Li1.4Al0.4Ge1.6(PO4)3 (80 wt%) composite electrolyte broaden the electrochemical window to 0–5 V, resulting in excellent performance for high‐voltage solid‐state Li‐metal batteries. Additionally, the thickness of electrolyte is below 25 μm.
Immune system evasion, distance tumor metastases, and increased cell proliferation are the main reasons for the progression of non-small cell lung cancer (NSCLC) and the death of NSCLC patients. ...Dysregulation of circular RNAs plays a critical role in the progression of NSCLC; therefore, further understanding the biological mechanisms of abnormally expressed circRNAs is critical to discovering novel, promising therapeutic targets for NSCLC treatment.
The expression of circular RNA fibroblast growth factor receptor 1 (circFGFR1) in NSCLC tissues, paired nontumor tissues, and cell lines was detected by RT-qPCR. The role of circFGFR1 in NSCLC progression was assessed both in vitro by CCK-8, clonal formation, wound healing, and Matrigel Transwell assays and in vivo by a subcutaneous tumor mouse assay. In vivo circRNA precipitation, RNA immunoprecipitation, and luciferase reporter assays were performed to explore the interaction between circFGFR1 and miR-381-3p.
Here, we report that circFGFR1 is upregulated in NSCLC tissues, and circFGFR1 expression is associated with deleterious clinicopathological characteristics and poor prognoses for NSCLC patients. Forced circFGFR1 expression promoted the migration, invasion, proliferation, and immune evasion of NSCLC cells. Mechanistically, circFGFR1 could directly interact with miR-381-3p and subsequently act as a miRNA sponge to upregulate the expression of the miR-381-3p target gene C-X-C motif chemokine receptor 4 (CXCR4), which promoted NSCLC progression and resistance to anti-programmed cell death 1 (PD-1)- based therapy.
Taken together, our results suggest the critical role of circFGFR1 in the proliferation, migration, invasion, and immune evasion abilities of NSCLC cells and provide a new perspective on circRNAs during NSCLC progression.
The layer‐structured MoS2 is a typical hydrogen evolution reaction (HER) electrocatalyst but it possesses poor activity for the oxygen evolution reaction (OER). In this work, a cobalt covalent doping ...approach capable of inducing HER and OER bifunctionality into MoS2 for efficient overall water splitting is reported. The results demonstrate that covalently doping cobalt into MoS2 can lead to dramatically enhanced HER activity while simultaneously inducing remarkable OER activity. The catalyst with optimal cobalt doping density can readily achieve HER and OER onset potentials of −0.02 and 1.45 V (vs reversible hydrogen electrode (RHE)) in 1.0 m KOH. Importantly, it can deliver high current densities of 10, 100, and 200 mA cm−2 at low HER and OER overpotentials of 48, 132, 165 mV and 260, 350, 390 mV, respectively. The reported catalyst activation approach can be adapted for bifunctionalization of other transition metal dichalcogenides.
A cobalt covalent doping catalyst activation approach to induce hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) bifunctionality of MoS2 is proposed and experimentally validated, demonstrating superior bifunctional electrocatalytic activities with great application potential for overall water splitting in alkaline media.
Self‐healable elastomers are extremely attractive due to their ability to prolong product lifetime. An additional function that could further expand their applications is strong adhesion force to ...clean and dusty surfaces. This study reports a series of autonomous self‐healable and highly adhesive elastomers (ASHA‐Elastomer) that are fabricated via a simple, efficient, and scalable process. The obtained elastomers exhibit outstanding mechanical properties with elongation at break up to 2102% and toughness (modulus of toughness) of 1.73 MJ m–3. The damaged ASHA‐Elastomer can autonomously self‐heal with full recovery of functionalities, and the healing process is not affected by the presence of water. The elastomers are found to possess an ultrahigh adhesion force up to 3488 N m−1, greatly outperforming previously reported self‐healing adhesive elastomers. Furthermore, the adhesion force of the ASHA‐Elastomer is negligibly affected by dust on the surface, in stark contrast with regular adhesive polymers that have adhesion strengths extremely sensitive to dust. The successful development of high‐toughness, autonomous self‐healable, and ultra‐adhesive elastomers will enable a wide range of applications with enhanced longevity and versatility, including their use in sealants, adhesives, and stretchable devices.
A series of self‐healing adhesive elastomers are fabricated via a simple, efficient, and scalable process. The obtained elastomers exhibit outstanding mechanical properties (extensibility 2102%, toughness 1.73 MJ m–3), and the damaged areas can autonomously self‐heal with full recovery. They also possess an ultrahigh adhesion force (3488 N m−1), greatly exceeding the reported self‐healing adhesive elastomers.
It is a great challenge to develop UV nonlinear optical (NLO) material due to the demanding conditions of strong second harmonic generation (SHG) intensity and wide band gap. The first ultraviolet ...NLO selenite material, Y3F(SeO3)4, has been obtained by control of the fluorine content in a centrosymmetric CaYF(SeO3)2. The two new compounds represent similar 3D structures composed of 3D yttrium open frameworks strengthened by selenite groups. CaYF(SeO3)2 has a large birefringence (0.138@532 nm and 0.127@1064 nm) and a wide optical band gap (5.06 eV). The non‐centrosymmetric Y3F(SeO3)4 can exhibit strong SHG intensity (5.5×KDP@1064 nm), wide band gap (5.03 eV), short UV cut‐off edge (204 nm) and high thermal stability (690 °C). So, Y3F(SeO3)4 is a new UV NLO material with excellent comprehensive properties. Our work shows that it is an effective method to develop new UV NLO selenite material by fluorination control of the centrosymmetric compounds.
The non‐centrosymmetric hydrogen‐free selenite Y3F(SeO3)4 was synthesized from the centrosymmetric CaYF(SeO3)2 by controlling the fluorine content. Y3F(SeO3)4 features large SHG intensity, wide band gap, short UV cut‐off edge and high thermal stability.
Dense depth perception is critical for autonomous driving and other robotics applications. However, modern LiDAR sensors only provide sparse depth measurement. It is thus necessary to complete the ...sparse LiDAR data, where a synchronized guidance RGB image is often used to facilitate this completion. Many neural networks have been designed for this task. However, they often naïvely fuse the LiDAR data and RGB image information by performing feature concatenation or element-wise addition. Inspired by the guided image filtering, we design a novel guided network to predict kernel weights from the guidance image. These predicted kernels are then applied to extract the depth image features. In this way, our network generates content-dependent and spatially-variant kernels for multi-modal feature fusion. Dynamically generated spatially-variant kernels could lead to prohibitive GPU memory consumption and computation overhead. We further design a convolution factorization to reduce computation and memory consumption. The GPU memory reduction makes it possible for feature fusion to work in multi-stage scheme. We conduct comprehensive experiments to verify our method on real-world outdoor, indoor and synthetic datasets. Our method produces strong results. It outperforms state-of-the-art methods on the NYUv2 dataset and ranks 1st on the KITTI depth completion benchmark at the time of submission. It also presents strong generalization capability under different 3D point densities, various lighting and weather conditions as well as cross-dataset evaluations. The code will be released for reproduction.
Metal-free three-dimensional perovskite ferroelectrics Ye, Heng-Yun; Tang, Yuan-Yuan; Li, Peng-Fei ...
Science (American Association for the Advancement of Science),
07/2018, Letnik:
361, Številka:
6398
Journal Article
Recenzirano
Odprti dostop
Inorganic perovskite ferroelectrics are widely used in nonvolatile memory elements, capacitors, and sensors because of their excellent ferroelectric and other properties. Organic ferroelectrics are ...desirable for their mechanical flexibility, low weight, environmentally friendly processing, and low processing temperatures. Although almost a century has passed since the first ferroelectric, Rochelle salt, was discovered, examples of highly desirable organic perovskite ferroelectrics are lacking. We found a family of metal-free organic perovskite ferroelectrics with the characteristic three-dimensional structure, among which MDABCO (
-methyl-
-diazabicyclo2.2.2octonium)-ammonium triiodide has a spontaneous polarization of 22 microcoulombs per square centimeter close to that of barium titanate (BTO), a high phase transition temperature of 448 kelvins (above that of BTO), and eight possible polarization directions. These attributes make it attractive for use in flexible devices, soft robotics, biomedical devices, and other applications.
Switchable materials play an invaluable role in signal processing and encryption of smart devices. The development of multifunctional materials that exhibit switching characteristics in multiple ...physical channels has attracted widespread attention. Now, two chiral thermochromic ferroelastic crystals (S‐CTA)2CuCl4 and (R‐CTA)2CuCl4 (CTA=3‐chloro‐2‐hydroxypropyltrimethylammonium) have been prepared with switchable properties in dielectricity, conductivity, second harmonic generation (SHG), piezoelectricity, ferroelasticity, chiral, and thermochromic properties. Compared with traditional phase‐transition materials with switching features, thermochromism brings additional spectral encryption possibilities for future information processing. To the best of our knowledge, this is the first chiral thermochromic ferroelastic that exhibits switching properties in seven physical channels. This work is expected to promote further exploration of multifunctional molecular switchable materials.
Seven switches: A pair of chiral thermochromic ferroelastics (R‐CTA)2CuCl4 and (S‐CTA)2CuCl4 (CTA=3‐chloro‐2‐hydroxypropyltrimethylammonium) exhibit switching properties in seven physical channels of dielectricity, conductivity, SHG, piezoelectric, ferroelastic, chiral, and thermochromic properties.
Soaring cases of coronavirus disease (COVID-19) are pummeling the global health system. Overwhelmed health facilities have endeavored to mitigate the pandemic, but mortality of COVID-19 continues to ...increase. Here, we present a mortality risk prediction model for COVID-19 (MRPMC) that uses patients' clinical data on admission to stratify patients by mortality risk, which enables prediction of physiological deterioration and death up to 20 days in advance. This ensemble model is built using four machine learning methods including Logistic Regression, Support Vector Machine, Gradient Boosted Decision Tree, and Neural Network. We validate MRPMC in an internal validation cohort and two external validation cohorts, where it achieves an AUC of 0.9621 (95% CI: 0.9464-0.9778), 0.9760 (0.9613-0.9906), and 0.9246 (0.8763-0.9729), respectively. This model enables expeditious and accurate mortality risk stratification of patients with COVID-19, and potentially facilitates more responsive health systems that are conducive to high risk COVID-19 patients.
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