Plastic recycling: A panacea or environmental pollution problem.

Increasing plastic waste is a critical global challenge to ecological and human health requiring focused solutions to reduce omnipresent plastic pollution in the environment. While recycling has been touted as one solution to counter plastic waste and resource utilization, it has been largely ineffective in offsetting the impact of rising global plastic production of more than 400 million metric tonnes annually, due to low global recycling rates of only 9%. Over three decades since implementing plastic resin codes, recycling has favoured thermoplastics, neglecting thermoset plastics. There is a constant need to enhance overall recycling efficiency by exploring advanced methods, as enormous gaps exist in fully unlocking the potential of plastic recycling. We identify critical gaps associated with plastic waste recycling and its potential environmental impacts. We discuss substantial progress in recycling technology, designs-for-recyclability with controlled chemical use, and economic incentives to expand markets for recycled plastics and to curb plastic leakage into the environment. Additionally, we highlight some emerging strategies and legally binding international policy instruments, such as the Global Plastics Treaty that require further development to reduce plastic waste and improve plastic recyclability.

Insights into One-Dimensional Thermoelectric Materials: A Concise Review of Nanowires and Nanotubes.

This brief review covers the thermoelectric properties of one-dimensional materials, such as nanowires and nanotubes. The highly localised peaks of the electronic density of states near the Fermi levels of these nanostructured materials improve the Seebeck coefficient. Moreover, quantum confinement leads to discrete energy levels and a modified density of states, potentially enhancing electrical conductivity. These electronic effects, coupled with the dominance of Umklapp phonon scattering, which reduces thermal conductivity in one-dimensional materials, can achieve unprecedented thermoelectric efficiency not seen in two-dimensional or bulk materials. Notable advancements include carbon and silicon nanotubes and Bi3Te2, Bi, ZnO, SiC, and Si1-xGex nanowires with significantly reduced thermal conductivity and increased ZT. In all these nanowires and nanotubes, efficiency is explored as a function of the diameter. Among these nanomaterials, carbon nanotubes offer mechanical flexibility and improved thermoelectric performance. Although carbon nanotubes theoretically have high thermal conductivity, the improvement of their Seebeck coefficient due to their low-dimensional structure can compensate for it. Regarding flexibility, economic criteria, ease of fabrication, and weight, carbon nanotubes could be a promising candidate for thermoelectric power generation.

Emergence of Interfacial Magnetism in Strongly-Correlated Nickelate-Titanate Superlattices.

Strongly-correlated transition-metal oxides are widely known for their various exotic phenomena. This is exemplified by rare-earth nickelates such as LaNiO3, which possess intimate interconnections between their electronic, spin, and lattice degrees of freedom. Their properties can be further enhanced by pairing them in hybrid heterostructures, which can lead to hidden phases and emergent phenomena. An important example is the LaNiO3/LaTiO3 superlattice, where an interlayer electron transfer has been observed from LaTiO3 into LaNiO3 leading to a high-spin state. However, macroscopic emergence of magnetic order associated with this high-spin state has so far not been observed. Here, by using muon spin rotation, x-ray absorption, and resonant inelastic x-ray scattering, direct evidence of an emergent antiferromagnetic order with high magnon energy and exchange interactions at the LaNiO3/LaTiO3 interface is presented. As the magnetism is purely interfacial, a single LaNiO3/LaTiO3 interface can essentially behave as an atomically thin strongly-correlated quasi-2D antiferromagnet, potentially allowing its technological utilization in advanced spintronic devices. Furthermore, its strong quasi-2D magnetic correlations, orbitally-polarized planar ligand holes, and layered superlattice design make its electronic, magnetic, and lattice configurations resemble the precursor states of superconducting cuprates and nickelates, but with an S→1 spin state instead.

Bone hierarchical organization through the lens of materials science: Present opportunities and future challenges.

Multiscale characterization is essential to better understand the hierarchical architecture of bone and an array of analytical methods contributes to exploring the various structural and compositional aspects. Incorporating X-ray tomography, X-ray scattering, vibrational spectroscopy, and atom probe tomography alongside electron microscopy provides a comprehensive approach, offering insights into the diverse levels of organization within bone. X-ray scattering techniques reveal information about collagen-mineral spatial relationships, while X-ray tomography captures 3D structural details, especially at the microscale. Electron microscopy, such as scanning and transmission electron microscopy, extends resolution to the nanoscale, showcasing intricate features such as collagen fibril organization. Additionally, atom probe tomography achieves sub-nanoscale resolution and high chemical sensitivity, enabling detailed examination of bone composition. Despite various technical challenges, a correlative approach allows for a comprehensive understanding of bone material properties. Real-time investigations through in situ and in operando approaches shed light on the dynamic processes in bone. Recently developed techniques such as liquid, in situ transmission electron microscopy provide insights into calcium phosphate formation and collagen mineralization. Mechanical models developed in the effort to link structure, composition, and function currently remain oversimplified but can be improved. In conclusion, correlative analytical platforms provide a holistic perspective of bone extracellular matrix and are essential for unraveling the intricate interplay between structure and composition within bone.

Impact of adsorption on thermal conductivity dynamics of adsorbate and adsorbent: Molecular dynamics study of methane and Cu-BTC.

Understanding changes in thermodynamic and transport properties during adsorption is crucial for the thermal management of metal-organic frameworks, which also imposes significant challenges for improved performance and energy density of adsorption system. Because of phase transitions in the intermolecular interactions involved in the adsorption phenomena, transport properties including the thermal conductivity exhibit interesting behaviors, yet fully understood. This study employs detailed molecular dynamics simulations to replicate the methane/Cu-BTC adsorption phenomenon for the evaluation of their thermal conductivities across different pressures and temperatures. The molecular simulations show that the thermal conductivities of both the adsorbent (Cu-BTC) and adsorbate (methane, adsorbed phase) vary notably during adsorption processes. Using the concepts of the change in the degree of free movements of the adsorbate molecules and atomic vibration of adsorbent, the reduction of the adsorbate thermal conductivity (∼70-93%) and increase thermal conductivity of the adsorbent (up to 3 times) in Cu-BTC+CH4 pair are explained.

A database of multi-principal element alloy phase-specific mechanical properties measured with nano-indentation.

Multi-principal element alloys (MPEAs) have been the focus of study and computationally-guided design for two reasons. MPEAs have shown high strengths and, the vast potential compositional space is more efficiently navigated with machine learning. In this article, we present data from 7385 indentation tests performed on 19 different MPEAs. Samples were arc melted, a thermodynamically complex process forming many distinct phases within a sample. The database was generated by performing hundreds of nanoindentation tests on a given sample and registering the location of those indents with local phase compositions measured with energy dispersive spectroscopy (EDS). The database contains the phases formed in the MPEA, the composition at the location of each indent, and the associated hardness (HV) and modulus for each indent. This data allows researchers targeting data-driven design of high strength systems to extract meaningful correlations between alloying composition, the resulting phases, and mechanical properties for future study.

Rapid start-up of carbon-free H2 production by ammonia oxidative decomposition over Co/Ce0.5Zr0.5O2 with microwave irradiation.

Hydrogen is a promising combustion improver for use with ammonia fuels, but a cost-effective method for easily producing hydrogen from ammonia at a high rate has yet to be developed. Here, we show that microwave irradiation instantly triggers oxidative decomposition of ammonia over a Co/Ce0.5Zr0.5O2 catalyst to produce hydrogen at a high rate. The microwave irradiation rapidly heats the inside of the catalyst from room temperature to the catalytic auto-ignition temperature of ammonia, thus initiating exothermic oxidative decomposition of ammonia to produce hydrogen. This method provides a highly efficient means of producing hydrogen for potential use in a carbon-free, ammonia-fueled power generation process.

Production of Martian fiber by in-situ resource utilization strategy.

Many countries and commercial organizations have shown great interest in constructing a Martian base. In situ resource utilization (ISRU) provides a cost-effective way to achieve this ambitious goal. In this article, we proposed to use Martian soil simulant to produce a fiber to satisfy material requirement for the construction of Martian base. The composition, melting behavior, and fiber forming process of the soil simulant was studied, and continuous fiber with maximum strength of 1320 MPa and elastic modulus of 99 GPa was obtained on a spinning facility. The findings of this study demonstrate the feasibility of ISRU to prepare Martian fiber from the soil on the Mars, offering a new way to obtain key materials for the construction of a Martian base.

Symmetry in thermal cycles and processes.

Symmetry analysis is a cutting-edge research approach in physics, yet its application in macroscopic energy systems remains limited. This study demonstrates its potential to provide valuable insights for a deeper understanding and development of thermodynamic cycles. This article first studies the symmetry of the proposed C-P diagrams and finds rich symmetries including reflection symmetry, translation symmetry, and rotational symmetry within Carnot cycles. Then, it emphasizes that one can use symmetry alone to prove that the highest efficiency for any cycle operating in a certain temperature range is the Carnot efficiency, without relying on the entropy concept in the second law of thermodynamics. Lastly, it is found that this symmetry analysis framework can also be used for thermal cycles with phase transitions, as exemplified by applying in Rankine cycles. This research not only contributes groundbreaking insights into unraveling the symmetry inherent in thermodynamic cycles, but also promotes symmetry analysis to be an alternative analysis mean.

Enhancing electrochemical performance of carbon fiber with lignin for flexible supercapacitors in PVA/H3PO4 matrix.

Lignin is an unlimited resource of an aromatic natural polymer useful for multiple applications, though its use remains limited due to the lack of technology developments. The introduction of lignin as coating of fibers and textile for composites is being recently studied with very promising results. Here, commercial woven carbon fibers (WCFs) have been superficially modified following an easy approach to introduce lignin and carbonize it to enhance the electrochemical performance of the fibers. The modified carbon fibers containing lignin were tested as electrodes using KCl 3 M as electrolyte, reaching 5 F/g at 10 mVs-1. A symmetric supercapacitor was fabricated using these electrodes, and a proof of concept based on a red and yellow light-emitting diode (LED) powered on and a bending test were done. The supercapacitor was able to recover 99% of its capacitance when returned to its original unbent position.

Advancing materials science through next-generation machine learning.

For over a decade, machine learning (ML) models have been making strides in computer vision and natural language processing (NLP), demonstrating high proficiency in specialized tasks. The emergence of large-scale language and generative image models, such as ChatGPT and Stable Diffusion, has significantly broadened the accessibility and application scope of these technologies. Traditional predictive models are typically constrained to mapping input data to numerical values or predefined categories, limiting their usefulness beyond their designated tasks. In contrast, contemporary models employ representation learning and generative modeling, enabling them to extract and encode key insights from a wide variety of data sources and decode them to create novel responses for desired goals. They can interpret queries phrased in natural language to deduce the intended output. In parallel, the application of ML techniques in materials science has advanced considerably, particularly in areas like inverse design, material prediction, and atomic modeling. Despite these advancements, the current models are overly specialized, hindering their potential to supplant established industrial processes. Materials science, therefore, necessitates the creation of a comprehensive, versatile model capable of interpreting human-readable inputs, intuiting a wide range of possible search directions, and delivering precise solutions. To realize such a model, the field must adopt cutting-edge representation, generative, and foundation model techniques tailored to materials science. A pivotal component in this endeavor is the establishment of an extensive, centralized dataset encompassing a broad spectrum of research topics. This dataset could be assembled by crowdsourcing global research contributions and developing models to extract data from existing literature and represent them in a homogenous format. A massive dataset can be used to train a central model that learns the underlying physics of the target areas, which can then be connected to a variety of specialized downstream tasks. Ultimately, the envisioned model would empower users to intuitively pose queries for a wide array of desired outcomes. It would facilitate the search for existing data that closely matches the sought-after solutions and leverage its understanding of physics and material-behavior relationships to innovate new solutions when pre-existing ones fall short.

Matching Together Living Cells and Prototissues: Will There Be Chemistry?

Scientific advancements in bottom-up synthetic biology have led to the development of numerous models of synthetic cells, or protocells. To date, research has mainly focused on increasing the (bio)chemical complexity of these bioinspired micro-compartmentalized systems, yet the successful integration of protocells with living cells remains one of the major challenges in bottom-up synthetic biology. In this review, we aim to summarize the current state of the art in hybrid protocell/living cell and prototissue/living cell systems. Inspired by recent breakthroughs in tissue engineering, we review the chemical, bio-chemical, and mechano-chemical aspects that hold promise for achieving an effective integration of non-living and living matter. The future production of fully integrated protocell/living cell systems and increasingly complex prototissue/living tissue systems not only has the potential to revolutionize the field of tissue engineering, but also paves the way for new technologies in (bio)sensing, personalized therapy, and drug delivery.

Evolution of Surface Chemistry in Two-Dimensional MXenes: From Mixed to Tunable Uniform Terminations.

Surface chemistry of MXenes is of great interest as the terminations can define the intrinsic properties of this family of materials. The diverse and tunable terminations also distinguish MXenes from many other 2D materials. Conventional fluoride-containing reagents etching approaches resulted in MXenes with mixed fluoro-, oxo-, and hydroxyl surface groups. The relatively strong chemical bonding of MXenes' surface metal atoms with oxygen and fluorine makes post-synthetic covalent surface modifications of such MXenes unfavorable. In this minireview, we focus on the recent advances in MXenes with uniform surface terminations. Unconventional methods, including Lewis acidic molten salt etching (LAMS) and bottom-up direct synthesis, have been proven successful in producing halide-terminated MXenes. These synthetic strategies have opened new possibilities for MXenes because weaker surface chemical bonds in halide-terminated MXenes facilitate post-synthetic covalent surface modifications. Both computational and experimental results on surface termination-dependent properties are summarized and discussed. Finally, we offer our perspective on the opportunities and challenges in this exciting research field.

Investigating the Mechanism of Triboluminescence: Insights from Structural and Electrostatic Characterization of Copper Thiocyanate Complexes.

Triboluminescence is a phenomenon in which light is generated through mechanical stress; it has emerging applications in stress-sensing devices. Although the prevailing mechanistic model indicates that light emits from charge separation and recombination in fracture planes arising from polar structures, its application in designing triboluminescent materials remains limited owing to numerous exceptions. This study provides insights into the essential requirements for triboluminescence by investigating the structural and electrostatic properties of fractured crystals of copper thiocyanate complexes. The examined fracture plane indicated that charge pairs (which are essential for light emission) form when intermolecular interactions are disrupted during fracturing. On the basis of the nature of these charges, we successfully suppressed triboluminescence by inhibiting the formation of intermolecular interactions disrupted in the examined complexes. Furthermore, we induced its re-emergence by creating an alternative fracture plane through controlled manipulation of the molecular network. This demonstrative deactivation and reactivation of triboluminescence underscores the critical role of intermolecular disruption in generating charge pairs, a prerequisite for triboluminescence.

Achieving consistency of flexible surface acoustic wave sensors with artificial intelligence.

Flexible surface acoustic wave technology has garnered significant attention for wearable electronics and sensing applications. However, the mechanical strains induced by random deformation of these flexible SAWs during sensing often significantly alter the specific sensing signals, causing critical issues such as inconsistency of the sensing results on a curved/flexible surface. To address this challenge, we first developed high-performance AlScN piezoelectric film-based flexible SAW sensors, investigated their response characteristics both theoretically and experimentally under various bending strains and UV illumination conditions, and achieved a high UV sensitivity of 1.71 KHz/(mW/cm²). To ensure reliable and consistent UV detection and eliminate the interference of bending strain on SAW sensors, we proposed using key features within the response signals of a single flexible SAW device to establish a regression model based on machine learning algorithms for precise UV detection under dynamic strain disturbances, successfully decoupling the interference of bending strain from target UV detection. The results indicate that under strain interferences from 0 to 1160 µÎµ the model based on the extreme gradient boosting algorithm exhibits optimal UV prediction performance. As a demonstration for practical applications, flexible SAW sensors were adhered to four different locations on spacecraft model surfaces, including flat and three curved surfaces with radii of curvature of 14.5, 11.5, and 5.8 cm. These flexible SAW sensors demonstrated high reliability and consistency in terms of UV sensing performance under random bending conditions, with results consistent with those on a flat surface.

The NOMAD mini-apps: A suite of kernels from ab initio electronic structure codes enabling co-design in high-performance computing.

This article introduces a suite of mini-applications (mini-apps) designed to optimise computational kernels in ab initio electronic structure codes. The suite is developed from flagship applications participating in the NOMAD Center of Excellence, such as the ELPA eigensolver library and the GW implementations of the exciting, Abinit, and FHI-aims codes. The mini-apps were identified by targeting functions that significantly contribute to the total execution time in the parent applications. This strategic selection allows for concentrated optimisation efforts. The suite is designed for easy deployment on various High-Performance Computing (HPC) systems, supported by an integrated CMake build system for straightforward compilation and execution. The aim is to harness the capabilities of emerging (post)exascale systems, which necessitate concurrent hardware and software development - a concept known as co-design. The mini-app suite serves as a tool for profiling and benchmarking, providing insights that can guide both software optimisation and hardware design. Ultimately, these developments will enable more accurate and efficient simulations of novel materials, leveraging the full potential of exascale computing in material science research.

ZnO nanoparticles encapsulated cellulose-lignin film for antibacterial and biodegradable food packaging.

Foodborne illness caused by consuming foods contaminated by pathogens remains threating to the public health. Despite considerable efforts of using renewable source materials, it is highly demanding to fabricate food packaging with multiple properties including eco-friendliness, bactericidal effect and biocompatibility. Here, sodium lignosulfonate (SL) and ZnO nanoparticles (ZnO NPs) were used as functional filler and structure components, respectively, on the cellulose nanofibers (CNFs)-based films, which endows the produced membrane (CNF/SL-ZnO) the UV-light blocking, antioxidant, and antimicrobial characteristics. Due to the interconnected polymeric structure, the prepared CNF/SL-ZnO films possessed considerable mechanical properties, thermal stability, and good moisture barrier capability. Moreover, the tested samples exhibited an improved shelf life in food packaging. Furthermore, metagenome analysis revealed superior biodegradability of obtained films with negligible side effect on the soil microenvironment. Therefore, the biocompatible, degradable, and antibacterial CNF/SL-ZnO film holds enormous potential for sustainable uses including food packaging.

Performance Assessment of Universal Machine Learning Interatomic Potentials: Challenges and Directions for Materials' Surfaces.

Machine learning interatomic potentials (MLIPs) are one of the main techniques in the materials science toolbox, able to bridge ab initio accuracy with the computational efficiency of classical force fields. This allows simulations ranging from atoms, molecules, and biosystems, to solid and bulk materials, surfaces, nanomaterials, and their interfaces and complex interactions. A recent class of advanced MLIPs, which use equivariant representations and deep graph neural networks, is known as universal models. These models are proposed as foundation models suitable for any system, covering most elements from the periodic table. Current universal MLIPs (UIPs) have been trained with the largest consistent data set available nowadays. However, these are composed mostly of bulk materials' DFT calculations. In this article, we assess the universality of all openly available UIPs, namely MACE, CHGNet, and M3GNet, in a representative task of generalization: calculation of surface energies. We find that the out-of-the-box foundation models have significant shortcomings in this task, with errors correlated to the total energy of surface simulations, having an out-of-domain distance from the training data set. Our results show that while UIPs are an efficient starting point for fine-tuning specialized models, we envision the potential of increasing the coverage of the materials space toward universal training data sets for MLIPs.