The power conversion efficiency of perovskite solar cells (PSCs) has ascended from 3.8% to 22.1% in recent years. ZnO has been well‐documented as an excellent electron‐transport material. However, ...the poor chemical compatibility between ZnO and organo‐metal halide perovskite makes it highly challenging to obtain highly efficient and stable PSCs using ZnO as the electron‐transport layer. It is demonstrated in this work that the surface passivation of ZnO by a thin layer of MgO and protonated ethanolamine (EA) readily makes ZnO as a very promising electron‐transporting material for creating hysteresis‐free, efficient, and stable PSCs. Systematic studies in this work reveal several important roles of the modification: (i) MgO inhibits the interfacial charge recombination, and thus enhances cell performance and stability; (ii) the protonated EA promotes the effective electron transport from perovskite to ZnO, further fully eliminating PSCs hysteresis; (iii) the modification makes ZnO compatible with perovskite, nicely resolving the instability of ZnO/perovskite interface. With all these findings, PSCs with the best efficiency up to 21.1% and no hysteresis are successfully fabricated. PSCs stable in air for more than 300 h are achieved when graphene is used to further encapsulate the cells.
Surface passivation of ZnO by a thin layer of MgO and protonated ethanolamine readily makes ZnO a very promising electron‐transporting material for creating efficient, hysteresis‐free and stable perovskite solar cells (PSCs). PSCs, stable in air for more than 300 h, are achieved when graphene is used to encapsulate the cells.
Dendrite growth of alkali metal anodes limited their lifetime for charge/discharge cycling. Here, we report near-perfect anodes of lithium, sodium, and potassium metals achieved by electrochemical ...polishing, which removes microscopic defects and creates ultra-smooth ultra-thin solid-electrolyte interphase layers at metal surfaces for providing a homogeneous environment. Precise characterizations by AFM force probing with corroborative in-depth XPS profile analysis reveal that the ultra-smooth ultra-thin solid-electrolyte interphase can be designed to have alternating inorganic-rich and organic-rich/mixed multi-layered structure, which offers mechanical property of coupled rigidity and elasticity. The polished metal anodes exhibit significantly enhanced cycling stability, specifically the lithium anodes can cycle for over 200 times at a real current density of 2 mA cm
with 100% depth of discharge. Our work illustrates that an ultra-smooth ultra-thin solid-electrolyte interphase may be robust enough to suppress dendrite growth and thus serve as an initial layer for further improved protection of alkali metal anodes.
Lithium metal anodes suffer from poor cycling stability and potential safety hazards. To alleviate these problems, Li thin‐film anodes prepared on current collectors (CCs) and Li‐free types of anodes ...that involve direct Li plating on CCs have received increasing attention. In this study, the atomic‐scale design of Cu‐CC surface lithiophilicity based on surface lattice matching of the bcc Li(110) and fcc Cu(100) faces as well as electrochemical achievement of Cu(100)‐preferred surfaces for smooth Li deposition with a low nucleation barrier is reported. Additionally, a purposely designed solid–electrolyte interphase is created for Li anodes prepared on CCs. Not only is a smooth planar Li thin film prepared, but a uniform Li plating/stripping on the skeleton of 3D CCs is achieved as well by high utilization of the surface and cavities of the 3D CCs. This work demonstrates surface electrochemistry approaches to construct stable Li metal–electrolyte interphases towards practical applications of Li anodes prepared on CCs.
Get in touch with lithium: The generation of surface lithiophilicity on planar and 3D Cu hosts for Li metal anodes is reported. Enabled by a lattice matching of Cu(100) and Li(110), smooth deposition of Li thin films and the creation of ultra‐smooth ultra‐thin SEI on the Cu hosts is made possible. This allows a high utilization of not only the surface but also cavities of the Cu hosts.
Electrochemical water splitting is a common way to produce hydrogen gas, but the sluggish kinetics of the oxygen evolution reaction (OER) significantly limits the overall energy conversion efficiency ...of water splitting. In this work, a highly active and stable, meso–macro hierarchical porous Ni3S4 architecture, enriched in Ni3+ is designed as an advanced electrocatalyst for OER. The obtained Ni3S4 architectures exhibit a relatively low overpotential of 257 mV at 10 mA cm−2 and 300 mV at 50 mA cm−2. Additionally, this Ni3S4 catalyst has excellent long‐term stability (no degradation after 300 h at 50 mA cm−2). The outstanding OER performance is due to the high concentration of Ni3+ and the meso–macro hierarchical porous structure. The presence of Ni3+ enhances the chemisorption of OH−, which facilitates electron transfer to the surface during OER. The hierarchical porosity increases the number of exposed active sites, and facilitates mass transport. A water‐splitting electrolyzer using the prepared Ni3S4 as the anode catalyst and Pt/C as the cathode catalyst achieves a low cell voltage of 1.51 V at 10 mA cm−2. Therefore, this work provides a new strategy for the rational design of highly active OER electrocatalysts with high valence Ni3+ and hierarchical porous architectures.
The Ni3S4 enriched with high‐valence Ni3+ enhances the chemisorption of OH−, which facilitates electron transfer from the OH− to the surface Ni sites during the oxygen evolution reaction. The hierarchical porous structure enables a high number of active sites and faster mass transfer.
Solid state potassium (K) metal batteries are intriguing in grid‐scale energy storage, benefiting from the low cost, safety, and high energy density. However, their practical applications are impeded ...by poor K/solid electrolyte (SE) interfacial contact and limited capacity caused by the low K self‐diffusion coefficient, dendrite growth, and intrinsically low melting point/soft features of metallic K. Herein, a fused‐modeling strategy using potassiophilic carbon allotropes molted with K is demonstrated that can enhance the electrochemical performance/stability of the system via promoting K diffusion kinetics (2.37 × 10−8 cm2 s−1), creating a low interfacial resistance (≈1.3 Ω cm2), suppressing dendrite growth, and maintaining mechanical/thermal stability at 200 °C. A homogeneous/stable K stripping/plating is consequently implemented with a high current density of 2.8 mA cm−2 (at 25 °C) and a record‐high areal capacity of 11.86 mAh cm−2 (at 0.2 mA cm−2). The enhanced K diffusion kinetics contribute to sustaining intimate interfacial contact, stabilizing the stripping/plating at high current densities. Full cells coupling ultrathin K–C composite anodes (≈50 µm) with Prussian blue cathodes and β/β″‐Al2O3 SEs deliver a high energy density of 389 Wh kg−1 with a retention of 94.4% after 150 cycles and fantastic performances at −20 to 120 °C.
An ultrathin K–10% reduced graphene oxide anode is constructed, delivering promoted K diffusion kinetics and mechanical/thermal stability at 200 °C, which creates an interfacial resistance (≈1.3 Ω cm2) with a current density of 2.8 mA cm−2 at 25 °C and an areal capacity of 11.86 mAh cm−2, enabling solid state potassium metal batteries operating at −20 to 120 °C.
Sodium metal anodes are poor due to the reversibility of Na plating/stripping, which hinders their practical applications. A strategy to form a sodiophilic Au–Na alloy interphase on a Cu current ...collector, involving a sputtered Au thin layer, is shown to enable efficient Na plating/stripping for a certain period of time. Herein, electrochemical behaviors of Na plating on different substrates are explored, and it is revealed that the sodiophilic interphase can be achieved universally by in situ formation of M–Na (M = Au, Sn, and Sb) alloys during Na plating prior to Na bulk deposition in the initial cycle. Moreover, it is found that repetitive alloying–dealloying leads to falling‐off of thin film sodiophilic materials and thus limits the lifespan of efficient Na cycling. Therefore, an approach is further developed by employing particles of sodiophilic materials combined with the control over the cutoff potential, which significantly improves the stability of Na plating/stripping process. Especially, the low‐cost Cu@Sn‐NPs and Cu@Sb‐MPs composite current collectors allow Na plating and stripping to cycle for 2000 and 1700 times with the average efficiency of 99.9% at 2 mA cm−2.
Stable Na plating/stripping electrochemical behaviors are achieved by using the in situ formed sodiophilic sodium‐metal (metal = Au, Sn, Sb) alloy interphase. By simultaneous control over the stripping cut‐off potential and employment of anchored metal particles, Na plating/stripping cycling is extended to 2000 times at 2 mA cm−2 with an average Coulombic efficiency of 99.9% on the sodium‐metal alloy‐based interphase.
Most human cancers develop from stem and progenitor cell populations through the sequential accumulation of various genetic and epigenetic alterations. Cancer stem cells have been identified from ...medulloblastoma (MB), but a comprehensive understanding of MB stemness, including the interactions between the tumor immune microenvironment and MB stemness, is lacking. Here, we employed a trained stemness index model based on an existent one‐class logistic regression (OCLR) machine‐learning method to score MB samples; we then obtained two stemness indices, a gene expression‐based stemness index (mRNAsi) and a DNA methylation‐based stemness index (mDNAsi), to perform an integrated analysis of MB stemness in a cohort of primary cancer samples (n = 763). We observed an inverse trend between mRNAsi and mDNAsi for MB subgroup and metastatic status. By applying the univariable Cox regression analysis, we found that mRNAsi significantly correlated with overall survival (OS) for all MB patients, whereas mDNAsi had no significant association with OS for all MB patients. In addition, by combining the Lasso‐penalized Cox regression machine‐learning approach with univariate and multivariate Cox regression analyses, we identified a stemness‐related gene expression signature that accurately predicted survival in patients with Sonic hedgehog (SHH) MB. Furthermore, positive correlations between mRNAsi and prognostic copy number aberrations in SHH MB, including MYCN amplifications and GLI2 amplifications, were detected. Analyses of the immune microenvironment revealed unanticipated correlations of MB stemness with infiltrating immune cells. Lastly, using the Connectivity Map, we identified potential drugs targeting the MB stemness signature. Our findings based on stemness indices might advance the development of objective diagnostic tools for quantitating MB stemness and lead to novel biomarkers that predict the survival of patients with MB or the efficacy of strategies targeting MB stem cells.
Here, we employed a trained stemness index model to perform an integrated analysis of medulloblastoma (MB) stemness. By combining the Lasso‐penalized Cox regression with univariate and multivariate Cox regression analyses, we identified a stemness‐related gene expression signature. Furthermore, positive correlations between gene expression‐based stemness index and prognostic copy number aberrations were detected. Analyses of the immune microenvironment revealed unanticipated correlations of MB stemness with infiltrating immune cells. Lastly, using the Connectivity Map, we identified potential drugs targeting the MB stemness signature.
Feature selection approaches based on mutual information can be roughly categorized into two groups. The first group minimizes the redundancy of features between each other. The second group ...maximizes the new classification information of features providing for the selected subset. A critical issue is that large new information does not signify little redundancy, and vice versa. Features with large new information but with high redundancy may be selected by the second group, and features with low redundancy but with little relevance with classes may be highly scored by the first group. Existing approaches fail to balance the importance of both terms. As such, a new information term denoted as Independent Classification Information is proposed in this paper. It assembles the newly provided information and the preserved information negatively correlated with the redundant information. Redundancy and new information are properly unified and equally treated in the new term. This strategy helps find the predictive features providing large new information and little redundancy. Moreover, independent classification information is proved as a loose upper bound of the total classification information of feature subset. Its maximization is conducive to achieve a high global discriminative performance. Comprehensive experiments demonstrate the effectiveness of the new approach.
Time-reversed evolution has substantial implications in physics, including applications in refocusing of classical waves or spins and fundamental studies such as quantum information scrambling. In ...quantum metrology, nonlinear interferometry based on time-reversal protocols supports entanglement-enhanced measurements without requiring low-noise detection. Despite the broad interest in this topic, it remains challenging to reverse the quantum dynamics of an interacting many-body system, which is typically realized by an (effective) sign flip of the system’s Hamiltonian. Here we present an approach that is broadly applicable to cyclic systems for implementing nonlinear interferometry without invoking time reversal. As time-reversed dynamics drives a system back to its starting point, we propose to accomplish the same by forcing the system to travel along a ‘closed loop’ instead of explicitly tracing back its antecedent path. Utilizing the quasiperiodic spin mixing dynamics in a three-mode 87Rb atomic spinor condensate, we implement such a closed-loop nonlinear interferometer and achieve a metrological gain of 5.01−0.76+0.76 decibels over the classical limit for a total of 26,500 atoms. Our approach unlocks the potential of nonlinear interferometry by allowing the dynamics to penetrate into the deep nonlinear regime, which gives rise to highly entangled non-Gaussian states.Nonlinear interferometry based on time reversal enables entanglement-enhanced measurements without the need for low-noise detection. An alternative approach now exploits cyclic dynamics and shows performance beyond the standard quantum limit.
•Automatic classification based on image recognition to reduce recycling labors.•Convolutional neural network (CNN) were transferred to identify the waste images.•Data augmentation improves the CNNs’ ...performance.•The optimized fully connected layer of CNNs enhances the CNNs’ performance.•Coarse features of the waste image by Grad-CAM validates the optimized CNN.
An automatic classification robot based on effective image recognition could help reduce huge labors of recycling tasks. Convolutional neural network (CNN) model, such as DenseNet121, improved the traditional image recognition technology and was the currently dominant approach to image recognition. A famous benchmark dataset, i.e., TrashNet, comprised of a total of 2527 images with six different waste categories was used to evaluate the CNNs’ performance. To enhance the accuracy of waste classification driven by CNNs, the data augmentation method could be adopted to do so, but fine-tuning optimally hyper-parameters of CNN's fully-connected-layer was never used. Therefore, besides data augmentation, this study aims to utilize a genetic algorithm (GA) to optimize the fully-connected-layer of DenseNet121 for improving the classification accuracy of DenseNet121 on TrashNet and proposes the optimized DenseNet121.
The results show that the optimized DenseNet121 achieved the highest accuracy of 99.6%, when compared with other studies’ CNNs. The data augmentation could perform higher efficiency on accuracy improvement of image classification than optimizing fully-connected-layer of DenseNet121 for TrashNet. To replace the function of the original classifier of DenseNet121 with fully-connected-layer can improve DenseNet121’s performance. The optimized DenseNet121 further improved the accuracy and demonstrated the efficiency of using GA to optimize the neuron number and the dropout rate of fully-connected-layer. Gradient-weighted class activation mapping helped highlight the coarse features of the waste image and provide additional insight into the explainability of optimized DenseNet121.
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