Massive spent batteries cause resource waste and environmental pollution. In the last decades, various approaches have been developed for the environmentally friendly recycling of waste batteries, as ...attractive secondary resources. In the present work, the recent progress in the recycling strategies is reviewed, with emphasis on the recovered products (metals and compounds) with high purity and the reutilization of the waste batteries as functional materials (electrode materials in energy storage devices, catalysts, sorbents, magnetic materials, alloys, etc.). Based on the literature summary, the future challenges are also prospected.
Liposomes have been considered promising and versatile drug vesicles. Compared with traditional drug delivery systems, liposomes exhibit better properties, including site-targeting, sustained or ...controlled release, protection of drugs from degradation and clearance, superior therapeutic effects, and lower toxic side effects. Given these merits, several liposomal drug products have been successfully approved and used in clinics over the last couple of decades. In this review, the liposomal drug products approved by the U.S. Food and Drug Administration (FDA) and European Medicines Agency (EMA) are discussed. Based on the published approval package in the FDA and European public assessment report (EPAR) in EMA, the critical chemistry information and mature pharmaceutical technologies applied in the marketed liposomal products, including the lipid excipient, manufacturing methods, nanosizing technique, drug loading methods, as well as critical quality attributions (CQAs) of products, are introduced. Additionally, the current regulatory guidance and future perspectives related to liposomal products are summarized. This knowledge can be used for research and development of the liposomal drug candidates under various pipelines, including the laboratory bench, pilot plant, and commercial manufacturing.
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.
Recently developed quantum algorithms address computational challenges in numerical analysis by performing linear algebra in Hilbert space. Such algorithms can produce a quantum state proportional to ...the solution of a
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-dimensional system of linear equations or linear differential equations with complexity
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, no such algorithm was previously known for differential equations with time-dependent coefficients. Here we develop a quantum algorithm for linear ordinary differential equations based on so-called spectral methods, an alternative to finite difference methods that approximates the solution globally. Using this approach, we give a quantum algorithm for time-dependent initial and boundary value problems with complexity
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Microloan markets allow individual borrowers to raise funding from multiple individual lenders. We use a unique panel data set that tracks the funding dynamics of borrower listings on Prosper.com, ...the largest microloan market in the United States. We find evidence of rational herding among lenders. Well-funded borrower listings tend to attract more funding after we control for unobserved listing heterogeneity and payoff externalities. Moreover, instead of passively mimicking their peers (irrational herding), lenders engage in active observational learning (rational herding); they infer the creditworthiness of borrowers by observing peer lending decisions and use publicly observable borrower characteristics to moderate their inferences. Counterintuitively, obvious defects (e.g., poor credit grades) amplify a listing's herding momentum, as lenders infer superior creditworthiness to justify the herd. Similarly, favorable borrower characteristics (e.g., friend endorsements) weaken the herding effect, as lenders attribute herding to these observable merits. Follow-up analysis shows that rational herding beats irrational herding in predicting loan performance.
This paper was accepted by Pradeep Chintagunta, marketing.
Recent progress in the surface modification of the clay minerals with polymers via physical adsorption and chemical grafting are reviewed. The surface modification of clay minerals especially with ...polymers could improve markedly their surface physical and chemical properties so the modified clay minerals could be applied as catalysts, adsorbents, in composite materials, and so on.
Carbon nanotubes (CNTs) are extensively explored in materials science due to their unique structure and consequent mystical properties. CNTs are enjoying increasing popularity as building blocks for ...novel drug delivery systems as well as for bioimaging and biosensing. The recent strategies to functionalize CNTs have resulted in the generation of biocompatible and water-soluble CNTs that are well suited for high treatment efficacy and minimum side effects for future cancer therapies with low drug doses. This review covers the latest advances in the strategies for the modification of CNTs (with inorganic nanoparticles, small organic molecules, polymers, or bioactive materials), with an emphasis on the development of functional biological nanointerfaces as drug vehicles, after a simple introduction of the toxicity of CNTs. The translation of these systems into clinical practice and an outlook into future approaches are also discussed.
Developing nonprecious oxygen evolution electrocatalysts that can work well at large current densities is of primary importance in a viable water‐splitting technology. Herein, a facile ultrafast (5 ...s) synthetic approach is reported that produces a novel, efficient, non‐noble metal oxygen‐evolution nano‐electrocatalyst that is composed of amorphous Ni–Fe bimetallic hydroxide film‐coated, nickel foam (NF)‐supported, Ni3S2 nanosheet arrays. The composite nanomaterial (denoted as Ni‐Fe‐OH@Ni3S2/NF) shows highly efficient electrocatalytic activity toward oxygen evolution reaction (OER) at large current densities, even in the order of 1000 mA cm−2. Ni‐Fe‐OH@Ni3S2/NF also gives an excellent catalytic stability toward OER both in 1 m KOH solution and in 30 wt% KOH solution. Further experimental results indicate that the effective integration of high catalytic reactivity, high structural stability, and high electronic conductivity into a single material system makes Ni‐Fe‐OH@Ni3S2/NF a remarkable catalytic ability for OER at large current densities.
An ultrafast (5 s) synthetic approach that produces a novel, nonprecious oxygen‐evolution electrocatalyst comprising a 3D hierarchical core@shell Ni‐Fe‐OH@Ni3S2 nanostructure supported on nickel foam is presented. The material integrates the structural and catalytic advantages of amorphous Ni–Fe–OH and Ni3S2 nanosheet arrays, possessing an excellent ability to efficiently and stably electrocatalyze the oxygen evolution reaction at large current densities.
MicrobiomeAnalyst is an easy-to-use, web-based platform for comprehensive analysis of common data outputs generated from current microbiome studies. It enables researchers and clinicians with little ...or no bioinformatics training to explore a wide variety of well-established methods for microbiome data processing, statistical analysis, functional profiling and comparison with public datasets or known microbial signatures. MicrobiomeAnalyst currently contains four modules: Marker-gene Data Profiling (MDP), Shotgun Data Profiling (SDP), Projection with Public Data (PPD), and Taxon Set Enrichment Analysis (TSEA). This protocol will first introduce the MDP module by providing a step-wise description of how to prepare, process and normalize data; perform community profiling; identify important features; and conduct correlation and classification analysis. We will then demonstrate how to perform predictive functional profiling and introduce several unique features of the SDP module for functional analysis. The last two sections will describe the key steps involved in using the PPD and TSEA modules for meta-analysis and visual exploration of the results. In summary, MicrobiomeAnalyst offers a one-stop shop that enables microbiome researchers to thoroughly explore their preprocessed microbiome data via intuitive web interfaces. The complete protocol can be executed in ~70 min.
•This work is the _rst overall review of recent deep learning-based lane detection methods.•Detailed description of representive methods from perpective of computer vision and pattern ...recognition.•Detailed description of convolution neural networks' architectures and loss functions that used in lanes detector.•Advantages of deep learning-based methods compared with traditional heuristic recognition-based methods.•Current challenges of existing deep learning-based methods and some possible directions to solve the problems.
Lane detection is an application of environmental perception, which aims to detect lane areas or lane lines by camera or lidar. In recent years, gratifying progress has been made in detection accuracy. To the best of our knowledge, this paper is the first attempt to make a comprehensive review of vision-based lane detection methods. First, we introduce the background of lane detection, including traditional lane detection methods and related deep learning methods. Second, we group the existing lane detection methods into two categories: two-step and one-step methods. Around the above summary, we introduce lane detection methods from the following two perspectives: (1) network architectures, including classification and object detection-based methods, end-to-end image-segmentation based methods, and some optimization strategies; (2) related loss functions. For each method, its contributions and weaknesses are introduced. Then, a brief comparison of representative methods is presented. Finally, we conclude this survey with some current challenges, such as expensive computation and the lack of generalization. And we point out some directions to be further explored in the future, that is, semi-supervised learning, meta-learning and neural architecture search, etc.
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