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All-inorganic metal halide perovskite is promising for highly efficient and thermally stable perovskite light-emitting diodes (PeLEDs). However, there is still great room for improvement in the film quality, including low coverage and high trap density, which play a vital role in achieving high-efficiency PeLEDs. In this work, lead acetate (Pb(Ac)2) was introduced into the perovskite precursor solution as an additive. Experimental results show that perovskite films deposited from a one-step anti-solvent free solution process with increased surface coverage and reduced trap density were obtained, leading to enhanced photoluminescence (PL) intensity. More than that, the valence band maximum (VBM) of perovskite films was reduced, bringing about a better energy level matching the work function of the hole-injection layer (HIL) poly (3,4-ethylenedioxythiophene)-poly (styrene sulfonate) (PEDOT: PSS), which is facilitated for the hole injection, leading to a decrease in the turn-on voltage (Vth) of PeLEDs from 3.4 V for the control device to 2.6 V. Finally, the external quantum efficiency (EQE) of the sky blue PeLEDs (at 484 nm) increased from 0.09% to 0.66%. The principles of Pb(Ac)2 were thoroughly investigated by using X-ray photoelectron spectroscopy (XPS) and Fourier transform infrared spectroscopy (FTIR). This work provides a simple and effective strategy for improving the morphology of perovskite and therefore the performance of PeLEDs.

With the global climate problem becoming increasingly severe, governments have adopted policies to encourage enterprises to invest in low-carbon technologies. However, the opacity of the carbon emission reduction process leads to incomplete consumer trust in low-carbon products as well as higher supply chain transaction costs. Based on this, this paper constructs Stackelberg game models with and without blockchain under different power structures and compares the impact of these models on low-carbon emission reduction decisions. The results show that: (1) blockchain does not necessarily improve enterprise profits and can only help enterprises maintain optimal profits within a certain range when the carbon emission cost is low; (2) when consumers’ environmental awareness is high, the blockchain can incentivize manufacturers to enhance carbon emission reduction, and it has an obvious promotional effect on retailers’ profits; and (3) the profit gap between enterprises in the supply chain is larger under different power structures, and the implementation of blockchain can coordinate profit distribution and narrow the gap between enterprises. Compared with the manufacturer-dominated model, the emission reduction in products is maximized under the retailer-dominated model. Our study provides theoretical support for the government to regulate greenhouse gas emissions as well as for the optimization of enterprises’ decision-making supported by blockchain.

Surface-enhanced Raman scattering (SERS) has garnered substantial attention due to its ability to achieve single-molecule sensitivity by utilizing metallic nanostructures to amplify the exceedingly weak Raman scattering process. However, the introduction of metal nanostructures can induce a background continuum which can reduce the ultimate sensitivity of SERS in ways that are not yet well understood. Here, we investigate the impact of laser irradiation on both Raman scattering and backgrounds from self-assembled monolayers within nanoparticle-on-mirror plasmonic nanocavities with variable geometry. We find that laser irradiation can reduce the height of the monolayer by inducing an irreversible change in molecular conformation. The resulting increased plasmon confinement in the nanocavities not only enhances the SERS signal, but also provides momentum conservation in the inelastic light scattering of electrons, contributing to the enhancement of the background continuum. The plasmon confinement can be modified by changing the size and the geometry of nanoparticles, resulting in a nanoparticle geometry-dependent background continuum in SERS. Our work provides new routes for further modifying the geometry of plasmonic nanostructures to improve SERS sensitivity.

In the context of developing the digital platform economy, trade-in programs have become an effective strategy for e-commerce platforms to stimulate consumption. Many head e-commerce platforms have launched their own trade-in programs. However, the existing research on trade-in programs is still stuck in the traditional trade-in model. The purpose of this study is to explore whether there is a new and more beneficial trade-in program. In this paper, we construct the Stackelberg game model between a brand owner and a B2C e-commerce platform under two trade-in programs and use optimization theory to obtain the equilibrium results of the model. The results indicate that the performance improvement of the new-generation product will promote the increase in two-generation products’ price under traditional trade-in programs, the price of the new-generation product will increase, and the price of the previous-generation product will decrease under new trade-in programs. The brand owner always prefers traditional trade-in to new trade-in. However, the e-commerce platform prefers traditional trade-in to new trade-in just when the previous-generation product is durable enough and the performance improvement of a new-generation product is small enough; otherwise, it prefers new trade-in to traditional trade-in. These findings are beneficial to the operational practices of e-commerce platforms and brand owners.

Defects in perovskite films are one of the main factors that affect the efficiency and stability of halide perovskite solar cells (PSCs). Uncoordinated ions (such as Pb2+, I−) act as trap states, causing the undesirable non-radiative recombination of photogenerated carriers. The formation of Lewis acid–base adducts in perovskite directly involves the crystallization process, which can effectively passivate defects. In this work, 4-(trifluoromethyl)-1H-imidazole (THI) was introduced into the perovskite precursor solution as a passivation agent. THI is a typical amphoteric compound that exhibits a strong Lewis base property due to its lone pair electrons. It coordinates with Lewis acid Pb2+, leading to the reduction in defect density and increase in crystallinity of perovskite films. Finally, the power conversion efficiency (PCE) of PSC increased from 16.49% to 18.97% due to the simultaneous enhancement of open-circuit voltage (VOC), short circuit current density (JSC) and fill factor (FF). After 30 days of storage, the PCE of the 0.16 THI PSC was maintained at 61.9% of its initial value, which was 44.3% for the control device. The working mechanism of THI was investigated. This work provides an attractive alternative method to passivate the defects in perovskite.

With the emergence of a large number of waste electronic products and the enhancement of social awareness of environmental protection, the recycling of waste electronic products has become one of the pressing issues of social concern. Government environmental regulation is an important policy to promote the development of the remanufacturing industry. In this paper, we study the government levies recycling and disposal fees on original products for environmental governance and establish two game models based on the perspective of maximizing social welfare with no government regulation and a tripartite liability system. The optimal decisions on wholesale, retail prices and quantity of original and remanufactured products, as well as the recycling and disposal fee are analyzed under both models. Based on the numerical results, the impact of the main parameter (such as the responsibility sharing ratio) on the decisions and profits of the parties is discussed. The results show that (1) the wholesale and retail prices of remanufactured products are not affected by government regulation; (2) the tripartite liability system can increase the output of remanufactured products and reduce the output of original products while cutting the profits of remanufacturing supply chain members, and increasing social welfare; (3) government’s optimal recycling and disposal fee is not related to the sharing ratio. The study can provide practitioners with suggestions for ways to develop environmental regulation.

Space launch projects are extremely risky, and any equipment failure or human error may lead to disastrous consequences. Probabilistic risk assessment (PRA) is beneficial to qualitative analysis of risk, but it has not been paid enough attention in risk analysis for space launch systems (SLSs). Compared with most qualitative risk analysis in this field, this paper proposes a risk analysis framework based on Bayesian network (BN) with fuzzy method, which is suitable for probabilistic risk analysis of SLS. This method establishes a risk analysis model of SLS based on statistics and expert experience and reduces the uncertainty of the model by using fuzzy theory. By predicting the system risk probabilities, diagnosing the key risk causes, determining the risk conduction path, and performing a sensitivity analysis, the proposed risk analysis framework is aimed at alleviating this drawback to deal more effectively with the uncertainties in the field of space launches. A case study of space launches demonstrates and verifies the proposed method, and it also provides guidance for similar engineering projects.

Antimicrobial resistance (AMR) poses a huge threat to public health. The development of novel antibiotics is an effective strategy to tackle AMR. Cyclic diadenylate monophosphate (c-di-AMP) has recently been identified as an essential signal molecule for some important bacterial pathogens involved in various bacterial physiological processes, leading to its synthase diadenylate cyclase becoming an attractive antimicrobial drug target. In this study, based on the enzymatic activity of diadenylate cyclase of Streptococcus suis (ssDacA), we established a high-throughput method of screening for ssDacA inhibitors. Primary screening with a compound library containing 1133 compounds identified IPA-3 (2,2′-dihydroxy-1,1′-dinapthyldisulfide) as an ssDacA inhibitor. High-performance liquid chromatography (HPLC) analysis further indicated that IPA-3 could inhibit the production of c-di-AMP by ssDacA in vitro in a dose-dependent manner. Notably, it was demonstrated that IPA-3 could significantly inhibit the growth of several Gram-positive bacteria which harbor an essential diadenylate cyclase but not E. coli, which is devoid of the enzyme, or Streptococcus mutans, in which the diadenylate cyclase is not essential. Additionally, the binding site in ssDacA for IPA-3 was predicted by molecular docking, and contains residues that are relatively conserved in diadenylate cyclase of Gram-positive bacteria. Collectively, our results illustrate the feasibility of ssDacA as an antimicrobial target and consider IPA-3 as a promising starting point for the development of a novel antibacterial.

Effects of pore diameters (100, 50, and 20 nm), concentration factors (1–8) and processing stages (1–5) on the transmission of major serum proteins (β-lactoglobulin and α-lactalbumin) and minor serum proteins (immunoglobulin (Ig) G, IgA, IgM, lactoferrin (LF), lactoperoxidase (LPO), xanthine oxidase (XO)) during ceramic microfiltration (MF) of skim milk were studied. Holstein skim milk was microfiltered at a temperature of 50 °C, a transmembrane pressure of 110 kPa and a crossflow velocity of 6.7 m/s, using a tubular single stainless steel module that consisted of three ceramic tubes, each with 19 channels (3.5 mm inner diameter) and a length of 0.5 m. For MF with 100 nm and 50 nm pore diameters, the recovery yield of major serum proteins in permeate was 44.3% and 44.1%, while the recovery yield of minor serum proteins was slightly less by 0%–8% than 50 nm MF. MF with 20 nm pore diameters showed a markedly lower (by 12%–45%) recovery yield for both major and minor serum proteins, corresponding with its lower membrane flux. Flux sharply decreased with an increasing concentration factor (CF) up to four, and thereafter remained almost unchanged. Compared to the decrease (88%) of flux, the transmission of major and minor serum proteins was decreased by 4%–15% from CF = one to CF = eight. With increasing processing stages, the flux gradually increased, and the recovery yield of both major and minor proteins in the permeate gradually decreased and reached a considerably low value at stage five. After four stages of MF with 100 nm pore diameter and a CF of four for each stage, the cumulative recovery yield of major serum proteins, IgG, IgA, IgM, LF, LPO, and XO reached 95.7%, 90.8%, 68.5%, 34.1%, 15.3%, 39.1% and 81.2% respectively.

Chemical diversification of type II topoisomerase (Topo II) inhibitors remains indispensable to extend their anti-tumor therapeutic values which are limited by their side effects. Herein, we designed and synthesized a novel series of benzimidazole-chalcone hybrids (BCHs). These BCHs showed good inhibitory effect in the Topo II mediated DNA relaxation assay and anti-proliferative effect in 4 tumor cell lines. 4d and 4n were the most potent, with IC50 values less than 5 μM, superior to etoposide. Mechanistic studies indicated that the BCHs functioned as non-intercalative Topo II catalytic inhibitors. Moreover, 4d and 4n demonstrated versatile properties against tumors, including inhibition on the colony formation and cell migration, and promotion of apoptosis of A549 cells. The structure-activity relationship and molecular docking analysis suggested possible contribution of the chalcone motif to the Topo II inhibitory and anti-proliferative potency. These results indicated that 4d and 4n could be promising lead compounds for further anti-tumor drug research.

As we enter the era of big data, we have to face big data generated by industrial systems that are massive, diverse, high-speed, and variability. In order to effectively deal with big data possessing these characteristics, deep learning technology has been widely used. However, the existing methods require great human involvement that is heavily depend on domain expertise and may thus be non-representative and biased from task to similar task, so for a wide variety of prognostic and health management (PHM) tasks, how to apply the developed deep learning algorithms to similar tasks to reduce the amount of development and data collection costs has become an urgent problem. Based on the idea of transfer learning and the structures of deep learning PHM algorithms, this paper proposes two transfer strategies via transferring different elements of deep learning PHM algorithms, analyzes the possible transfer scenarios in practical application, and proposes transfer strategies applicable in each scenario. At the end of this paper, the deep learning algorithm of bearing fault diagnosis based on convolutional neural networks (CNN) is transferred based on the proposed method, which was carried out under different working conditions and for different objects, respectively. The experiments verify the value and effectiveness of the proposed method and give the best choice of transfer strategy.

This study describes the first general optimization model for complex systems with uncertain parameters and decision variables represented as intervals in CDM forestry projects. We work through a specific example of the optimization method developed for a Clean Development Mechanism (CDM) forestry project in Inner Mongolia, China. This model is designed to optimize the carbon sink capacity of the new forests, and can deal with uncertainties in the carbon sink capacity, average annual rainfall, ecological parameters, and biological characteristics of tree species. The uncertain inputs are presented in the form of intervals, as are several of the optimized output variables. Compared with the project’s originally recommended scheme, the optimized model will absorb and fix between 1,142 and 885,762 tonnes of extra carbon dioxide. Moreover, the ecological and environmental benefits of the project are also raised to various extents.

This review presents an overall discussion on the morphology analysis and optimization for perovskite (PVSK) solar cells. Surface morphology and energy alignment have been proven to play a dominant role in determining the device performance. The effect of the key parameters such as solution condition and preparation atmosphere on the crystallization of PVSK, the characterization of surface morphology and interface distribution in the perovskite layer is discussed in detail. Furthermore, the analysis of interface energy level alignment by using X-ray photoelectron spectroscopy and ultraviolet photoelectron spectroscopy is presented to reveals the correlation between morphology and charge generation and collection within the perovskite layer, and its influence on the device performance. The techniques including architecture modification, solvent annealing, etc. were reviewed as an efficient approach to improve the morphology of PVSK. It is expected that further progress will be achieved with more efforts devoted to the insight of the mechanism of surface engineering in the field of PVSK solar cells.