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Interfacial reactions between electrode and electrolyte are critical, either beneficial or detrimental, for the performance of rechargeable batteries. The general approaches of controlling interfacial reactions are either applying a coating layer on cathode or modifying the electrolyte chemistry. Here we demonstrate an approach of modification of interfacial reactions through dilute lattice doping for enhanced battery properties. Using atomic level imaging, spectroscopic analysis and density functional theory calculation, we reveal aluminum dopants in lithium nickel cobalt aluminum oxide are partially dissolved in the bulk lattice with a tendency of enrichment near the primary particle surface and partially exist as aluminum oxide nano-islands that are epitaxially dressed on the primary particle surface. The aluminum concentrated surface lowers transition metal redox energy level and consequently promotes the formation of a stable cathode-electrolyte interphase. The present observations demonstrate a general principle as how the trace dopants modify the solid-liquid interfacial reactions for enhanced performance. The electrode/electrolyte interface plays a critical role in a Li-ion battery. Here the authors report that Al doping can tailor the interfacial reactions to lead to enhanced structural stability and cyclability of cathode. Al dopants form not only lattice solid solution but also Al 2 O 3 islands on the surface.

Ammonia represents a promising liquid fuel for hydrogen storage, but its large-scale application is limited by the need for precious metal ruthenium (Ru) as catalyst. Here we report on highly efficient ammonia decomposition using novel high-entropy alloy (HEA) catalysts made of earth abundant elements. Quinary CoMoFeNiCu nanoparticles are synthesized in a single solid-solution phase with robust control over the Co/Mo atomic ratio, including those ratios considered to be immiscible according to the Co-Mo bimetallic phase diagram. These HEA nanoparticles demonstrate substantially enhanced catalytic activity and stability for ammonia decomposition, with improvement factors achieving >20 versus Ru catalysts. Catalytic activity of HEA nanoparticles is robustly tunable by varying the Co/Mo ratio, allowing for the optimization of surface property to maximize the reactivity under different reaction conditions. Our work highlights the great potential of HEAs for catalyzing chemical transformation and energy conversion reactions. Alloys are important materials for catalysis but are usually limited by miscibility gaps present in their phase diagrams. Here the authors break this limitation by developing high-entropy alloy catalysts made of five earth-abundant elements and demonstrate great catalytic enhancements for ammonia decomposition.

Environmental protection tax is an important tool for directing environmentally friendly growth in heavily polluting enterprises, but existing research has yet to provide consistent conclusions on whether and how environmental protection tax can promote green innovation in heavily polluting industries. The paper uses a double difference model based on data from Chinese listed companies in heavily polluting industries from 2012 to 2021 to empirically investigate whether environmental protection tax drives green innovation behavior of heavily polluting enterprises. The findings show that the environmental protection tax increases the degree of green innovation in heavily polluting enterprises, primarily through the anti-driving effect, in which an increase in environmental management expenses forces firms to increase their R&D investment, which improves the degree of green technical innovation. Furthermore, the environmental protection tax has a strong promotion effect on heavy polluters’ green innovation for state-owned enterprises and those in growing period or located in high marketization regions. However, this promotion effect is insignificant for non-state-owned enterprises and those in recession period, and environmental protection tax hinders green innovation of enterprises in mature period and those located in low marketization regions. Accordingly, it is suggested to improve preferential tax policies, increase investment in corporate green innovation and strengthen the supervision of environmental tax.

Due to the availability of materials and low cost for production, fused deposition modeling is becoming the most widely used additive manufacturing (AM) technology. However, the reasonable choice of process parameters for FDM is a significant task that directly affects the performance of the printed part. Therefore, it is necessary to investigate the influences of various process parameters on the quality characteristics of the components. The objectives of this study are to thoroughly review the current state of research that characterizes, estimates the effects of process parameters on mechanical properties, and summarizes the conclusions of existing works. In addition, some general issues of the presented research are summarized, and the need for future development is also emphasized. Finally, the research proposes several areas that deserve further study in this field.

Natural products (NPs) are critical sources of drug molecules for decades. About two-thirds of natural antibiotics are produced by Streptomyces. Streptomyces have a large number of secondary metabolite biosynthetic gene clusters (SM-BGCs) that may encode NPs. However, most of these BGCs are silent under standard laboratory conditions. Hence, activation of these silent BGCs is essential to current natural products discovery research. In this review, we described the commonly used strategies for silent BGC activation in Streptomyces from two aspects. One focused on the strategies applied in heterologous host, including methods to clone and reconstruct BGCs along with advances in chassis engineering; the other focused on methods applied in native host which includes engineering of promoters, regulatory factors, and ribosomes. With the metabolic network being elucidated more comprehensively and methods optimized more high-thoroughly, the discovery of NPs will be greatly accelerated.

This paper made the first attempt to summarize the rules from a regional perspective and use panel data to explore the carbon Kuznets curve (CKC) between e-commerce and carbon dioxide emissions. The impact of online shopping on carbon emission has mixed conclusions. No CKC tests set mainly focuses on the e-commerce sector, which can help this research determine the relationship between e-commerce and carbon emissions. From a macro point of view, we examine both developed and developing regions by testing the CKC hypothesis. We try to explain it by exploring the econometric relationship between e-commerce and CO 2 emissions. At first, we attempt to accurately measure the CO 2 emissions by carefully distinguishing the carbon emission increments caused by the primary energy resulting from the secondary energy. Then, we use panel data collected from different Chinese cities during 2001–2017. The analyzed variables are stationary at their first differences with the LLC test, IPS test, Fisher-ADF test, Fisher-PP test, CADF, and CIPS unit root tests. The analyzed variables are cointegrated by employing the Pedroni panel cointegration test, the Kao panel cointegration test, and the Westerlund panel cointegration test. Using the DOLS, we also find that increases in trade openness decrease carbon emissions while increases in foreign direct investment (FDI) and market size contribute to the level of emissions. The quadratic-shape CKC hypothesis is supported for China, Eastern China, and Western China, and it is an inverted “U” shape. The cubic-form CKC is supported for Central China, and it is an “N” shape. Our study provides important insights for enacting regional and country-level e-commerce regulations and reducing carbon dioxide emissions.

Background Microplastics (MPs), which are smaller in size and difficult to degrade, can be easily ingested by marine life and enter mammals through the food chain. Our previous study demonstrated that following acute exposure to MPs, the serum testosterone content reduced and sperm quality declined, resulting in male reproductive dysfunction in mice. However, the toxic effect of long-term exposure to MPs at environmental exposure levels on the reproductive system of mammals remains unclear. Results In vivo, mice were given drinking water containing 100 μg/L and 1000 μg/L polystyrene MPs (PS-MPs) with particle sizes of 0.5 μm, 4 μm, and 10 μm for 180 consecutive days. We observed alterations in testicular morphology and reductions in testosterone, LH and FSH contents in serum. In addition, the viability of sperm was declined and the rate of sperm abnormality was increased following exposure to PS-MPs. The expression of steroidogenic enzymes and StAR was downregulated in testis tissues. In vitro, we used primary Leydig cells to explore the underlying mechanism of the decrease in testosterone induced by PS-MPs. First, we discovered that PS-MPs attached to and became internalized by Leydig cells. And then we found that the contents of testosterone in the supernatant declined. Meanwhile, LHR, steroidogenic enzymes and StAR were downregulated with concentration-dependent on PS-MPs. We also confirmed that PS-MPs decreased StAR expression by inhibiting activation of the AC/cAMP/PKA pathway. Moreover, the overexpression of LHR alleviated the reduction in StAR and steroidogenic enzymes levels, and finally alleviated the reduction in testosterone induced by PS-MPs. Conclusions PS-MPs exposure resulted in alterations in testicular histology, abnormal spermatogenesis, and interference of serum hormone secretion in mice. PS-MPs induced a reduction in testosterone level through downregulation of the LH-mediated LHR/cAMP/PKA/StAR pathway. In summary, our study showed that chronic exposure to PS-MPs resulted in toxicity of male reproduction under environmental exposure levels, and these potential risks may ring alarm bells of public health. Graphical abstract

Medical imaging can assess the tumor and its environment in their entirety, which makes it suitable for monitoring the temporal and spatial characteristics of the tumor. Progress in computational methods, especially in artificial intelligence for medical image process and analysis, has converted these images into quantitative and minable data associated with clinical events in oncology management. This concept was first described as radiomics in 2012. Since then, computer scientists, radiologists, and oncologists have gravitated towards this new tool and exploited advanced methodologies to mine the information behind medical images. On the basis of a great quantity of radiographic images and novel computational technologies, researchers developed and validated radiomic models that may improve the accuracy of diagnoses and therapy response assessments. Here, we review the recent methodological developments in radiomics, including data acquisition, tumor segmentation, feature extraction, and modelling, as well as the rapidly developing deep learning technology. Moreover, we outline the main applications of radiomics in diagnosis, treatment planning and evaluations in the field of oncology with the aim of developing quantitative and personalized medicine. Finally, we discuss the challenges in the field of radiomics and the scope and clinical applicability of these methods.

Distant metastasis (DM) is the main cause of treatment failure in locally advanced rectal cancer. Adjuvant chemotherapy is usually used for distant control. However, not all patients can benefit from adjuvant chemotherapy, and particularly, some patients may even get worse outcomes after the treatment. We develop and validate an MRI-based radiomic signature (RS) for prediction of DM within a multicenter dataset. The RS is proved to be an independent prognostic factor as it not only demonstrates good accuracy for discriminating patients into high and low risk of DM in all the four cohorts, but also outperforms clinical models. Within the stratified analysis, good chemotherapy efficacy is observed for patients with pN2 disease and low RS, whereas poor chemotherapy efficacy is detected in patients with pT1–2 or pN0 disease and high RS. The RS may help individualized treatment planning to select patients who may benefit from adjuvant chemotherapy for distant control. Distant metastasis (DM) is the main cause of treatment failure in locally advanced rectal cancer. Here, the authors developed and validated a radiomic signature (RS) for prediction of DM within a multicenter dataset, and suggest that it may help with stratification of patients who might benefit from adjuvant chemotherapy for DM.

ObjectivesTo investigate whether CT-based radiomics signature can predict KRAS/NRAS/BRAF mutations in colorectal cancer (CRC).MethodsThis retrospective study consisted of a primary cohort (n = 61) and a validation cohort (n = 56) with pathologically confirmed CRC. Patients underwent KRAS/NRAS/BRAF mutation tests and contrast-enhanced CT before treatment. A total of 346 radiomics features were extracted from portal venous-phase CT images of the entire primary tumour. Associations between the genetic mutations and clinical background, tumour staging, and histological differentiation were assessed using univariate analysis. RELIEFF and support vector machine methods were performed to select key features and build a radiomics signature.ResultsThe radiomics signature was significantly associated with KRAS/NRAS/BRAF mutations (P < 0.001). The area under the curve, sensitivity, and specificity for predicting KRAS/NRAS/BRAF mutations were 0.869, 0.757, and 0.833 in the primary cohort, respectively, while they were 0.829, 0.686, and 0.857 in the validation cohort, respectively. Clinical background, tumour staging, and histological differentiation were not associated with KRAS/NRAS/BRAF mutations in both cohorts (P>0.05).ConclusionsThe proposed CT-based radiomics signature is associated with KRAS/NRAS/BRAF mutations. CT may be useful for analysis of tumour genotype in CRC and thus helpful to determine therapeutic strategies.Key Points• Key features were extracted from CT images of the primary colorectal tumour.• The proposed radiomics signature was significantly associated with KRAS/NRAS/BRAF mutations.• In the primary cohort, the proposed radiomics signature predicted mutations.• Clinical background, tumour staging, and histological differentiation were unable to predict mutations.