Energy storage systems, such as flow batteries, are essential for integrating variable renewable energy sources into the electricity grid. While a primary goal of increased renewable energy use on the grid is to mitigate environmental impact, the production of enabling technologies like energy storage systems causes environmental impact. Thus, understanding the impact of producing energy storage systems is crucial for determining the overall environmental performance of renewable energy from a systems perspective. In this study, the environmental impact associated with the production of emerging flow battery technologies is evaluated in an effort to inform materials selection and component design decisions. The production of three commercially available flow battery technologies is evaluated and compared on the basis of eight environmental impact categories, using primary data collected from battery manufacturers on the battery production phase including raw materials extraction, materials processing, manufacturing and assembly. In the baseline scenario, production of all-iron flow batteries led to the lowest impact scores in six of the eight impact categories such as global warming potential, 73 kg CO 2 eq/kWh; and cumulative energy demand, 1090 MJ/kWh. While the production of vanadium redox flow batteries led to the highest impact values for six categories including global warming potential, 184 kg CO 2 eq/kWh; and cumulative energy demand, 5200 MJ/kWh. Production of zinc-bromine flow batteries had the lowest values for ozone depletion, and freshwater ecotoxicity, and the highest value for abiotic resource depletion. The analysis highlight that the relative environmental impact of producing the three flow battery technologies varies with different system designs and materials selection choices. For example, harmonization of the battery system boundary led to freshwater eutrophication and freshwater ecotoxicity values for vanadium redox flow batteries lower than the values for zinc-bromine flow batteries. Regarding alternative material use strategies, we conclude that vanadium redox flow batteries exhibit the lowest potential in four of the eight impact categories including global warming potential at 61 kg CO 2 eq/kWh. In zinc-bromine flow batteries, the titanium-based bipolar plate contributes higher environmental impact compared to carbon-based materials, and the polymer resins used in all-iron flow batteries could be replaced with material with lower potential for ecotoxicity. Overall, the analysis reveals the sources of potential environmental impact, due to the production of flow battery materials, components and systems. The findings from this study are urgently needed before these batteries become widely deployed in the renewable energy sector. Furthermore, our results indicate that materials options change the relative environmental impact of producing the three flow batteries and provide the potential to significantly reduce the environmental impact associated with flow battery production and deployment.

Antimicrobial peptides (AMPs) are a class of molecules that play an essential role in innate immune regulation. The Brevinin-1 family are AMPs that show strong pharmacological and antimicrobial potential. A novel peptide, B1A, was designed based on the primary structure of brevinin-1PLb and brevinin-1PLc. Subsequently, a synthesised replicate was subjected to a series of bioassays and was found to display antimicrobial activity. However, it also displayed high levels of haemolysis in a horse red blood cell haemolytic assay, suggesting potential toxicity. Therefore, we rationally designed a number of B1A analogues with aim of retaining antimicrobial activity, lowering toxicity, and to explore the structure–activity relationship of its N-terminus. B1A and its analogues still retained the “Rana Box” and the FLP-motif, which is a feature of this subfamily. However, the introduction of Lys and Trp residues into the peptide sequences revealed that antimicrobial activity of these analogues remained unchanged once the hydrophobicity and the charge reached the threshold. Hence, the idea that the hydrophobicity saturation in different situations is related to antimicrobial activity can be understood via the structure–activity relationship. Meanwhile, it could also be the starting point for the generation of peptides with specific antimicrobial activity.

Amphibian skin secretions are remarkable sources of novel bioactive peptides. Among these, antimicrobial peptides have demonstrated an outstanding efficacy in killing microorganisms via a general membranolytic mechanism, which may offer the prospect of solving specific target-driven antibiotic resistance. Here, the discovery of a novel defensive peptide is described from the skin secretion of the African frog, Kassina senegalensis. Named kassinatuerin-3, it was identified through a combination of “shot-gun” cloning and MS/MS fragmentation sequencing. Subsequently, a synthetic replicate was subjected to biofunctional evaluation. The results indicated that kassinatuerin-3 possessed antimicrobial activity against Gram-positive bacteria but no effect against Gram-negative bacteria. Additionally, it was active in biofilm eradication on S. aureus and MRSA and in the antiproliferation of selected cancer cell lines. Moreover, it had a very mild hemolytic effect, which demonstrated a high therapeutic index for kassinatuerin-3. Collectively, although kassinatuerin-3 did not demonstrate remarkable bioactivities compared with other natural or synthetic antimicrobial peptides (AMPs), it offered a new insight into the design of antimicrobial derivatives.

Electronic waste, with printed circuit boards (PCBs) at its heart, is the fastest-growing category of hazardous solid waste in the world. New materials, in particular biobased materials, show great promise in solving some of the sustainability and toxicity problems associated with PCBs, although several challenges still prevent their practical application.

IntroductionSkin cancer is one of the most common types of cancer. An accessible tool to the public can help screening for malign lesion. We aimed to develop a deep learning model to classify skin lesion using clinical images and meta information collected from smartphones.MethodsA deep neural network was developed with two encoders for extracting information from image data and metadata. A multimodal fusion module with intra-modality self-attention and inter-modality cross-attention was proposed to effectively combine image features and meta features. The model was trained on tested on a public dataset and compared with other state-of-the-art methods using five-fold cross-validation.ResultsIncluding metadata is shown to significantly improve a model's performance. Our model outperformed other metadata fusion methods in terms of accuracy, balanced accuracy and area under the receiver-operating characteristic curve, with an averaged value of 0.768±0.022, 0.775±0.022 and 0.947±0.007.ConclusionA deep learning model using smartphone collected images and metadata for skin lesion diagnosis was successfully developed. The proposed model showed promising performance and could be a potential tool for skin cancer screening.