It has long been claimed that a better understanding of human or social dimensions of environmental issues will improve conservation. The social sciences are one important means through which researchers and practitioners can attain that better understanding. Yet, a lack of awareness of the scope and uncertainty about the purpose of the conservation social sciences impedes the conservation community's effective engagement with the human dimensions. This paper examines the scope and purpose of eighteen subfields of classic, interdisciplinary and applied conservation social sciences and articulates ten distinct contributions that the social sciences can make to understanding and improving conservation. In brief, the conservation social sciences can be valuable to conservation for descriptive, diagnostic, disruptive, reflexive, generative, innovative, or instrumental reasons. This review and supporting materials provides a succinct yet comprehensive reference for conservation scientists and practitioners. We contend that the social sciences can help facilitate conservation policies, actions and outcomes that are more legitimate, salient, robust and effective.
This review presents a comprehensive and systematic study of the field of plant biostimulants and considers the fundamental and innovative principles underlying this technology. The elucidation of the biological basis of biostimulant function is a prerequisite for the development of science-based biostimulant industry and sound regulations governing these compounds. The task of defining the biological basis of biostimulants as a class of compounds, however, is made more complex by the diverse sources of biostimulants present in the market, which include bacteria, fungi, seaweeds, higher plants, animals and humate-containing raw materials, and the wide diversity of industrial processes utilized in their preparation. To distinguish biostimulants from the existing legislative product categories we propose the following definition of a biostimulant as “a formulated product of biological origin that improves plant productivity as a consequence of the novel or emergent properties of the complex of constituents, and not as a sole consequence of the presence of known essential plant nutrients, plant growth regulators, or plant protective compounds.” The definition provided here is important as it emphasizes the principle that biological function can be positively modulated through application of molecules, or mixtures of molecules, for which an explicit mode of action has not been defined. Given the difficulty in determining a “mode of action” for a biostimulant, and recognizing the need for the market in biostimulants to attain legitimacy, we suggest that the focus of biostimulant research and validation should be upon proof of efficacy and safety and the determination of a broad mechanism of action, without a requirement for the determination of a specific mode of action. While there is a clear commercial imperative to rationalize biostimulants as a discrete class of products, there is also a compelling biological case for the science-based development of, and experimentation with biostimulants in the expectation that this may lead to the identification of novel biological molecules and phenomenon, pathways and processes, that would not have been discovered if the category of biostimulants did not exist, or was not considered legitimate.
Atomic clusters, consisting of a few to a few thousand atoms, have emerged over the past 40 years as the ultimate nanoparticles, whose structure and properties can be controlled one atom at a time. One of the early motivations in studying clusters was to understand how the properties of matter evolve as a function of size, shape, and composition. Over the past few decades, more than 200 000 papers have been published in this field. These studies have not only led to a considerable understanding of this evolution from clusters to crystals, but also have revealed many unusual size-specific properties that make cluster science an interdisciplinary field on its own, bridging physics, chemistry, materials science, biology, and medicine. More importantly, the possibility of creating a new class of materials, composed of clusters instead of atoms as building blocks, has fueled the hope that one can synthesize materials from the bottom-up with unique and tailored properties. This Review focuses on the properties that set clusters apart from their corresponding bulk. Furthermore, this Review describes how different electron-counting rules can lead to the design of stable clusters, mimicking the chemistry of atoms. We highlight the potential of these "superatoms" as building blocks of cluster-assembled materials. Specifically, we emphasize cluster-inspired materials for energy applications. The concluding section includes a summary of the salient features of clusters, potential challenges that remain, and an outlook for the future of cluster science.
Abstract Machine learning plays an important role in accelerating the discovery and design process for novel electrochemical energy storage materials. This review aims to provide the state-of-the-art and prospects of machine learning for the design of rechargeable battery materials. After illustrating the key concepts of machine learning and basic procedures for applying machine learning in rechargeable battery materials science, we focus on how to obtain the most important features from the specific physical, chemical and/or other properties of material by using wrapper feature selection method, embedded feature selection method, and the combination of these two methods. And then, the applications of machine learning in rechargeable battery materials design and discovery are reviewed, including the property prediction for liquid electrolytes, solid electrolytes, electrode materials, and the discovery of novel rechargeable battery materials through component prediction and structure prediction. More importantly, we discuss the key challenges related to machine learning in rechargeable battery materials science, including the contradiction between high dimension and small sample, the conflict between the complexity and accuracy of machine learning models, and the inconsistency between learning results and domain expert knowledge. In response to these challenges, we propose possible countermeasures and forecast potential directions of future research. This review is expected to shed light on machine learning in rechargeable battery materials design and property optimization.
There are difficult problems in materials science where the general concepts might be understood but which are not as yet amenable to scientific treatment. We are at the same time told that good engineering has the responsibility to reach objectives in a cost and time-effective way. Any model which deals with only a small part of the required technology is therefore unlikely to be treated with respect. Neural network analysis is a form of regression or classification modelling which can help resolve these difficulties whilst striving for longer term solutions. This paper begins with an introduction to neural networks and contains a review of some applications of the technique in the context of materials science.
Abstract The rigidity of conventional microwave absorbers impedes their reliable integration with complex curved surfaces and deformable components. However, these capabilities remain indispensable for electromagnetic stealth and miniaturized electromagnetic compatibility (EMC) applications that rely on advanced conformal wave-manipulation. To overcome these challenges, we present a chainmail-inspired conformal and switchable microwave metamaterial absorber, conveniently fabricated via 3D printing. The structure exhibits a broadband effective absorption bandwidth (EAB) from 6.2 to 17.6 GHz, while its interlocking topological design enables curvature adaptation without deformation of the absorbing parts. After conformal shaping, the average absorptivity decreases by only 0.049, demonstrating markedly enhanced robustness compared with conventional designs. Furthermore, we integrate the microwave absorber with elastic bands to achieve reversible switching between expanded and contracted states, extending the minimum operating frequency. The optimally configured design achieves a cumulative bandwidth (union of the EABs achieved at 24 cm and 27 cm configurations) from 4.6 to 18 GHz, circumventing the Rozanov limit through dynamic switching. This work provides a practical strategy for realizing switchable ultra-wideband conformal microwave absorbers in diverse electromagnetic applications.
To retrieve and compare scientific data of simulations and experiments in materials science, data needs to be easily accessible and machine readable to qualify and quantify various materials science phenomena. The recent progress in open science leverages the accessibility to data. However, a majority of information is encoded within scientific documents limiting the capability of finding suitable literature as well as material properties. This manuscript showcases an automated workflow, which unravels the encoded information from scientific literature to a machine readable data structure of texts, figures, tables, equations and meta-data, using natural language processing and language as well as vision transformer models to generate a machine-readable database. The machine-readable database can be enriched with local data, as e.g. unpublished or private material data, leading to knowledge synthesis. The study shows that such an automated workflow accelerates information retrieval, proximate context detection and material property extraction from multi-modal input data exemplarily shown for the research field of microstructural analyses of face-centered cubic single crystals. Ultimately, a Retrieval-Augmented Generation (RAG) based Large Language Model (LLM) enables a fast and efficient question answering chat bot.
Adib Bazgir, Rama chandra Praneeth Madugula, Yuwen Zhang
We introduce a multicrossmodal LLM-agent framework motivated by the growing volume and diversity of materials-science data ranging from high-resolution microscopy and dynamic simulation videos to tabular experiment logs and sprawling literature archives. While recent AI efforts have accelerated individual tasks such as property prediction or image classification, they typically treat each modality in isolation, leaving rich cross-modal correlations unexplored and forcing researchers to perform laborious manual integration. Moreover, existing multimodal foundation models often require expensive retraining or fine-tuning on domain data, and current multi-agent systems in materials informatics address only narrow subtasks. To overcome these obstacles, we design a coordinated team of specialized LLM agents, each equipped with domain-adapted prompts and plugins that project their outputs into a shared embedding space. A dynamic gating mechanism then weights and merges these insights, enabling unified reasoning over heterogeneous inputs without ever modifying the underlying LLM weights. We validate our approach on challenging case studies and demonstrate substantial gains in retrieval accuracy (85%), captioning fidelity, and integrated coverage (35%) compared to single-modality and zero-shot baselines. Our work paves the way for AI digital researchers capable of bridging data silos and accelerating the materials-discovery cycle. The code is available at https://github.com/adibgpt/Multicrossmodal-Autonomous-Materials-Science-Agent.
Multicomponent metallic glasses (MGs) are a fascinating class of advanced alloys known for their exceptional properties such as limit-approaching strength, high hardness and corrosion resistance, and near-net-shape castability. One important question regarding these materials that remains unanswered is how the different elements and atomic bonds within them control their strength and deformability. Here, we present a detailed visual and statistical analysis of the behaviors of various elements and atomic bonds in the Zr<sub>47</sub>Cu<sub>46</sub>Al<sub>7</sub> (at%) MG during a uniaxial tensile test (in the z-direction) simulated using molecular dynamics. Specifically, we investigate the identities of atoms undergoing significant shear strain, and the averaged bond lengths, projected z-lengths, and z-angles (angles with respect to the z-direction) of all the atomic bonds as functions of increasing strain. We show that, prior to yielding, the Zr element and the intermediate (Zr-Zr, Cu-Al) and stronger (Zr-Al, Zr-Cu) bonds dominate the elastic deformation and strength, while the Cu and Al elements and the weaker Al-Al and Cu-Cu bonds contribute more to the highly localized shear transformation. The significant reconstruction, as signified by the cessation of bond-length increment and bond-angle decrement, of the intermediate and the stronger bonds triggers yielding of the material. After yielding, all the elements and bonds participate in the plastic deformation while the stronger bonds contribute more to the residual strength and the ultimate (fracture) strain. The results provide new insights into the atomic mechanisms underlying the mechanical behavior of multicomponent MGs, and may assist in the future design of MG compositions towards better combination of strength and deformability.
Dmytro V. Pavlenko, Daria V. Tkach, Yevgen V. Vyshnepolskyi
et al.
The technology of manufacturing aluminum alloy workpieces using additive friction stir deposition (AFS-D) has been thoroughly investigated. The ambiguous influence of deformation processing modes on the material density was found. Examination of the microstructure in the central zone of the specimens reveals the absence of microdefects typically associated with workpieces obtained through casting or powder metallurgy methods. It has been observed that the distribution of microhardness is significantly affected by the direction of specimen construction, with approximately a 20% difference in values between the periphery and the central part of the specimen. Specimens produced using the AFS-D method exhibit a homogeneous microstructure characteristic of deformable aluminum alloys. Notably, a uniform distribution of intermetallides on the specimen surface has been identified. This outcome is likely a result of the alloy undergoing recrystallization during the severe plastic deformation process, leading to the formation of an ultradisperse structure. It is important to emphasize that the selection of technological parameters for AFS-D should consider not only the magnitude of pressure and deformation but also the deformation speed.
Materials of engineering and construction. Mechanics of materials
Bibhu P. Sahu, Mohsen T. Andani, Arkajit Ghosh
et al.
The crystallography of the eutectic Al-Si microstructure in both unmodified and Sr (0.2 wt.%)-modified hypereutectic Al-20 wt.% Si alloys, processed via arc-melting and laser surface remelting, has been comprehensively characterized using transmission electron microscopy and electron diffraction. Although, under as-cast conditions, specific orientations between different planes of Al and Si, satisfying defined orientation relationships (ORs), have been investigated within the flake morphology, the rapid solidification induced by laser surface remelting results in a notable transformation from a flake morphology to nanocrystalline Si fibers dispersed in an Al matrix. Consequently, this transformation results in a mis-orientation of the interface between the eutectic Al and Si phases, preventing the formation of orientation relationships, thus promoting the formation of faceted interfaces exhibiting substantial lattice disregistry.
Nithya S. George, Gurwinder Singh, Rohan Bahadur
et al.
Abstract Hybrid ion capacitors (HICs) have aroused extreme interest due to their combined characteristics of energy and power densities. The performance of HICs lies hidden in the electrode materials used for the construction of battery and supercapacitor components. The hunt is always on to locate the best material in terms of cost‐effectiveness and overall optimized performance characteristics. Functionalized biomass‐derived porous carbons (FBPCs) possess exquisite features including easy synthesis, wide availability, high surface area, large pore volume, tunable pore size, surface functional groups, a wide range of morphologies, and high thermal and chemical stability. FBPCs have found immense use as cathode, anode and dual electrode materials for HICs in the recent literature. The current review is designed around two main concepts which include the synthesis and properties of FBPCs followed by their utilization in various types of HICs. Among monovalent HICs, lithium, sodium, and potassium, are given comprehensive attention, whereas zinc is the only multivalent HIC that is focused upon due to corresponding literature availability. Special attention is also provided to the critical factors that govern the performance of HICs. The review concludes by providing feasible directions for future research in various aspects of FBPCs and their utilization in HICs.
We introduce a novel continued pre-training method, MELT (MatEriaLs-aware continued pre-Training), specifically designed to efficiently adapt the pre-trained language models (PLMs) for materials science. Unlike previous adaptation strategies that solely focus on constructing domain-specific corpus, MELT comprehensively considers both the corpus and the training strategy, given that materials science corpus has distinct characteristics from other domains. To this end, we first construct a comprehensive materials knowledge base from the scientific corpus by building semantic graphs. Leveraging this extracted knowledge, we integrate a curriculum into the adaptation process that begins with familiar and generalized concepts and progressively moves toward more specialized terms. We conduct extensive experiments across diverse benchmarks to verify the effectiveness and generality of MELT. A comprehensive evaluation convincingly supports the strength of MELT, demonstrating superior performance compared to existing continued pre-training methods. The in-depth analysis also shows that MELT enables PLMs to effectively represent materials entities compared to the existing adaptation methods, thereby highlighting its broad applicability across a wide spectrum of materials science.