Materials Science Research Articles

Ultrafast Laser Manipulation of In-Lattice Plasmonic Nanoparticles.

Plasmonic nanoparticles enable manipulation and enhancement of light fields at deep subwavelength scales, leading to structures and devices for diverse applications in optics. Despite hybrid plasmonic materials display remarkable optical properties due to interactions between components in nanoproximity, scalable production of plasmonic nanostructures within a single-crystalline matrix to achieve an ideal plasmon-crystal interface remains challenging. Here, a novel approach is presented to realize efficient manipulation of in-lattice plasmonic nanoparticles. Employing ultrafast-laser-driven plasmonic nanolithography, metallic nanoparticles with controllable morphology are precisely defined in the crystalline lattice of yttrium aluminum garnet (YAG) crystal. Through direct ion implantation, hybrid plasmonic material composed of nanoparticles embedded in a sub-surface amorphous YAG layer is created. Subsequently, femtosecond laser pulses guide formation and reshaping of plasmonic nanoparticles from the amorphous layer into the single-crystalline matrix along direction of light propagation, facilitated by a plasmon-mediated evolution of laser energy deposition. By tailoring resonance modes and optimizing the coupling between structured particle assemblies, a range of applications including polarization-dependent absorption and nonlinearity, controllable photoluminescence, and structural color generation is demonstrated. This research introduces a new approach for fabricating advanced optical materials featuring in-lattice plasmonic nanostructures, paving the way for the development of diverse functional photonic devices.

The role of a digital twin in supporting criminal investigations - a case report about a possible abuse.

As part of a comprehensive analysis, this case report presents a possible case of child maltreatment that can serve as a basis for forensic and medical examiner investigations. This case concerns the death of an infant who was approximately two months old. During a routine examination by the pediatrician at the end of May 2021, the child was found to have a normal head circumference of 31 cm. No other abnormalities were noted. On June 19, 2021, the child died, and an autopsy revealed a head circumference of 44 cm and a subdural hematoma as the cause of death. Questions arose as to who might have abused the child and when. The only evidence was a low-quality cell phone video taken by the child's parents on June 13, 2021, six days before the child's death, in which the child could be seen lying on a pillow. It was necessary to determine whether the child in this video already had an unnatural head circumference. This study presents a novel workflow that demonstrates how to analyze and deal with low quality video to answer questions like the above. The workflow demonstrates the creation of 3D scenes from digital image and video material. These 3D scenes can be used for object measurement and to support forensic and medical investigations. In the present case, where only low quality smartphone images were available, the presented workflow was used to create a 3D scene of the child lying on the pillow. In this 3D scene, it was possible to determine the child's head circumference. These measurements support the findings of the medical examiner (dated June 24, 2021) and confirm the suspicion that possible child abuse had already taken place on June 13, 2021. The innovative approach makes it possible to identify evidence of possible abuse based on a specific point in time, in this case the child's private footage. It also demonstrates the potential of 3D scene reconstruction in complex forensic and medical scenarios.

Graphitic Carbon Nitride Enters the Scene: A Promising Versatile Tool for Biomedical Applications.

Graphitic carbon nitride (g-C3N4), since the pioneering work on visible-light photocatalytic water splitting in 2009, has emerged as a highly promising advanced material for environmental and energetic applications, including photocatalytic degradation of pollutants, photocatalytic hydrogen generation, and carbon dioxide reduction. Due to its distinctive two-dimensional structure, excellent chemical stability, and distinctive optical and electrical properties, g-C3N4 has garnered a considerable amount of interest in the field of biomedicine in recent years. This review focuses on the fundamental properties of g-C3N4, highlighting the synthesis and modification strategies associated with the interfacial structures of g-C3N4-based materials, including heterojunction, band gap engineering, doping, and nanocomposite hybridization. Furthermore, the biomedical applications of these materials in various domains, including biosensors, antimicrobial applications, and photocatalytic degradation of medical pollutants, are also described with the objective of spotlighting the unique advantages of g-C3N4. A summary of the challenges faced and future prospects for the advancement of g-C3N4-based materials is presented, and it is hoped that this review will inspire readers to seek further new applications for this material in biomedical and other fields.

Rational Design of Deep Learning Networks Based on a Fusion Strategy for Improved Material Property Predictions.

The success of machine learning in predicting material properties is largely dependent on the design of the model. However, the current designs of deep learning models in materials science have the following prominent problems. First, the model design lacks a rational guidance strategy and heavily relies on a large amount of trial and error. Second, numerous deep learning models are utilized across various fields, each with its own advantages and disadvantages. Therefore, it is important to incorporate a fusion strategy to fully leverage them and further expand the design strategies of the models. To address these problems, we analyze that the main reason is the lack of a new feedback method rich in physical insights. In this study, we developed a feedback method called the Chemical Environment Clustering Vector (CECV) of compounds at different thresholds, which is rich in physical insights. Based on CECV, we rationally designed the Long Short-Term Memory and Gated Recurrent Unit fused with Deep Convolutional Neural Network (L-G-DCNN) to explore the field of structure-agnostic material property predictions. L-G-DCNN accurately captures the interactions between elements in compounds, enabling more accurate and efficient predictions of the material properties. Our results demonstrate that the performance of the L-G-DCNN surpasses the current state-of-the-art structure-agnostic models across 28 benchmark data sets, exhibiting superior sample efficiency and faster convergence speed. By employing different visualization methods, we demonstrate that the fusion strategy based on CECV significantly enhances the comprehension of the L-G-DCNN model design and provides a fresh perspective for researchers in the field of materials informatics.

Regiodivergent Metal-Catalyzed Oxidative Alkynylation of 2-Arylthiazoles with Terminal Alkynes under Air Conditions.

Regiodivergent transition-metal-catalyzed oxidative C5- and ortho-alkynylation of 2-arylthiazoles have been demonstrated. Namely, Pd(II)-catalysis selectively generated C5-alkynylated products from the reaction of 2-arylthiazoles and terminal alkynes. In contrast, Ru(II)-catalysis exclusively provided ortho-alkynylated products from the same substrates. This protocol features a wide substrate scope, good functional group tolerance, high atom-economy, and exclusive regioselectivity. The alkynylated products can be readily converted into highly valuable synthons, which hold potential for applications in the fields of medicinal chemistry and materials science.

Xiaoyan Tang.

"The most important factor in the choice of my current research topic was the urgent need to develop eco-friendly alternatives to traditional polymers, aiming to reduce environmental impact and promote sustainability in materials science… My favorite time of day is my journey home after work, a peaceful moment to reflect and unwind from the day's activities, while eagerly anticipating seeing my son…" Find out more about Xiaoyan Tang in her Introducing… Profile.

Selective nitrogen insertion into aryl alkanes

Molecular structure-editing through nitrogen insertion offers more efficient and ingenious pathways for the synthesis of nitrogen-containing compounds, which could benefit the development of synthetic chemistry, pharmaceutical research, and materials science. Substituted amines, especially nitrogen-containing alkyl heterocyclic compounds, are widely found in nature products and drugs. Generally, accessing these compounds requires multiple steps, which could result in low efficiency. In this work, a molecular editing strategy is used to realize the synthesis of nitrogen-containing compounds using aryl alkanes as starting materials. Using derivatives of O-tosylhydroxylamine as the nitrogen source, this method enables precise nitrogen insertion into the Csp2-Csp3 bond of aryl alkanes. Notably, further synthetic applications demonstrate that this method could be used to prepare bioactive molecules with good efficiency and modify the molecular skeleton of drugs. Furthermore, a plausible reaction mechanism involving the transformation of carbocation and imine intermediates has been proposed based on the results of control experiments.

Solving Advanced Task-Specific Problems in Measurement Sciences with Generative AI.

The Generative Pre-Trained Transformer known as ChatGPT-4 has undergone extensive pretraining on a diverse data set, enabling it to generate coherent and contextually relevant text based on the input it receives. This capability allows it to perform tasks from answering questions and has attracted significant interest in material sciences, synthetic chemistry, and drug discovery. In this work, we posed four advanced task-specific problems to ChatGPT, which were recently published in leading journals for topics in analytical chemistry, spectroscopy, bioimage super-resolution, and electrochemistry. ChatGPT-4 successfully implemented the four ideas after assigning the "persona" to the AI and posing targeted questions. We show two cases where "unguided" ChatGPT could complete the assignments with minimal human direction. The construction of a microwave spectrum from a free induction curve and super-resolution of bioimages was accomplished using this approach. Two other specific tasks, correcting a complex baseline with morphological operations of set theory and estimating the diffusion current, required additional input, e.g., equations and specific directions from the user. In each case, the MATLAB code was eventually generated by ChatGPT-4 even when the original authors did not provide any code themselves. We show that a validation test must be implemented to detect and correct mistakes made by ChatGPT-4, followed by feedback correction. These approaches will pave the way for open and transparent science and eliminate the black boxes in measurement science when it comes to advanced data processing.

Advanced materials and deepwater asset life cycle management: A strategic approach for enhancing offshore oil and gas operations

Advanced materials and deepwater asset life cycle management: A strategic approach for enhancing offshore oil and gas operations

Rational Strategies to Improve the Efficiency of 2D Perovskite Solar Cells.

In the quest for durable photovoltaic devices, 2D halide perovskites have emerged as a focus of extensive research. However, the reduced dimension in structure is accompanied by inferior optical-electrical properties, such as widened band gap, enhanced exciton binding energy, and obstructed charge transport. As a result, the efficiency of 2D perovskite solar cells (PSCs) lags significantly behind their 3D counterparts. To overcome these constraints, extensive investigations into materials and processing techniques are pursued rigorously to augment the efficiency of 2D PSCs. Herein, The cutting-edge delve into developments in 2D PSCs, with a focus on chemical and material engineering, as well as their structure and photovoltaic properties. The review starts with an introduction of the crystal structure, followed by the key evaluation criteria of 2D PSCs. Then, the strategies around solution chemical engineering, processing technique, and interface optimization, to simultaneously boost efficiency and stability are systematically discussed. Finally, the challenges and perspectives associated with 2D perovskites to provide insights into potential improvements in photovoltaic performance will be outlined.

Photo-Clickable Triazine-Trione Thermosets as Promising 3D Scaffolds for Tissue Engineering Applications.

There is an overwhelming demand for new scaffolding materials for tissue engineering (TE) purposes. Polymeric scaffolds have been explored as TE materials; however, their high glass transition state (Tg) limits their applicability. In this study, a novel materials platform for fabricating TE scaffolds is proposed based on solvent-free two-component heterocyclic triazine-trione (TATO) formulations, which cure at room temperature via thiol-ene/yne photochemistry. Three ester-containing thermosets, TATO-1, TATO-2, and TATO-3, are used for the fabrication of TE scaffolds including rigid discs, elastic films, microporous sponges, and 3D printed objects. After 14 days' incubation the materials covered a wide range of properties, from the soft TATO-2 having a compression modulus of 19.3MPa and a Tg of 30.4°C to the hard TATO-3 having a compression modulus of 411MPa and a Tg of 62.5°C. All materials exhibit micro- and nano-surface morphologies suited for bone tissue engineering, and in vitro studies found them all to be cytocompatible, supporting fast cell proliferation while minimizing cell apoptosis and necrosis. Moreover, bone marrow-derived mesenchymal stem cells on the surface of the materials are successfully differentiated into osteoblasts, adipocytes, and neuronal cells, underlining the broad potential for the biofabrication of TATO materials for TE clinical applications.

Design of Co-Cured Multi-Component Thermosets with Enhanced Heat Resistance, Toughness, and Processability via a Machine Learning Approach.

Designing heat-resistant thermosets with excellent comprehensive performance has been a long-standing challenge. Co-curing of various high-performance thermosets is an effective strategy, however, the traditional trial-and-error experiments have long research cycles for discovering new materials. Herein, a two-step machine learning (ML) assisted approach is proposed to design heat-resistant co-cured resins composed of polyimide (PI) and silicon-containing arylacetylene (PSA), that is, poly(silicon-alkyne imide) (PSI). First, two ML prediction models are established to evaluate the processability of PIs and their compatibility with PSA. Then, another two ML models are developed to predict the thermal decomposition temperature and flexural strength of the co-cured PSI resins. The optimal molecular structures and compositions of PSI resins are high-throughput screened. The screened PSI resins are experimentally verified to exhibit enhanced heat resistance, toughness, and processability. The research framework established in this work can be generalized to the rational design of other advanced multi-component polymeric materials.

A holistic platform for accelerating sorbent-based carbon capture.

Reducing carbon dioxide (CO2) emissions urgently requires the large-scale deployment of carbon-capture technologies. These technologies must separate CO2 from various sources and deliver it to different sinks1,2. The quest for optimal solutions for specific source-sink pairs is a complex, multi-objective challenge involving multiple stakeholders and depends on social, economic and regional contexts. Currently, research follows a sequential approach: chemists focus on materials design3 and engineers on optimizing processes4,5, which are then operated at a scale that impacts the economy and the environment. Assessing these impacts, such as the greenhouse gas emissions over the plant's lifetime, is typically one of the final steps6. Here we introduce the PrISMa (Process-Informed design of tailor-made Sorbent Materials) platform, which integrates materials, process design, techno-economics and life-cycle assessment. We compare more than 60 case studies capturing CO2 from various sources in 5 global regions using different technologies. The platform simultaneously informs various stakeholders about the cost-effectiveness of technologies, process configurations and locations, reveals the molecular characteristics of the top-performing sorbents, and provides insights on environmental impacts, co-benefits and trade-offs. By uniting stakeholders at an early research stage, PrISMa accelerates carbon-capture technology development during this critical period as we aim for a net-zero world.

Biomimetic Materials Research, from Interest-driven to Society-serving: An interview with Shu-Hong Yu

Biomimetic Materials Research, from Interest-driven to Society-serving: An interview with Shu-Hong Yu

Benign and Selective Amination of Lignins towards Aromatic Biobased Building Blocks with Primary Amines.

Lignin is a widely available second-generation biopolymer and the main potential source of renewable aromatic building blocks. Lignin-based polyamines offer great potential in applications based on chemical and materials sciences. However, common aminations techniques for lignin usually involve toxic chemicals and generate hindered and low reactivity amines. In this study, we developed two new, simple, and benign 2-step methodologies for the elaboration of lignin-based polyamines from different technical lignins (kraft, soda and organosolv) with a selectivity towards reactive primary amines. These methods involve grafting amide groups onto lignin followed by a hydrolysis step. Non-toxic heterocyclic compounds N-acetyl-2-oxazolidinone and 2-methyl-2-oxazoline were used as amidation agents. Hydrolysis was performed in acetone-water mixtures. Reactions were studied on model compounds and optimized on lignins. Aminated lignins were fully characterized and primary amines were quantified using quantitative 19F NMR. Our methods generated aminated lignins with low apparent molar masses and high solubility in water and solvents. Nitrogen contents of the products ranged between 2.0 and 3.5 mmol/g with reactive primary amines counts up to 1.7 mmol/g. These soluble and reactive lignin-based polyamines offer great potential as a replacement for fossil-based polyamines in e.g., the synthesis of aromatic polymer materials or as potential chelating, antibacterial agents.

Soft matter physics of the ground beneath our feet.

The soft part of the Earth's surface - the ground beneath our feet - constitutes the basis for life and natural resources, yet a general physical understanding of the ground is still lacking. In this critical time of climate change, cross-pollination of scientific approaches is urgently needed to better understand the behavior of our planet's surface. The major topics in current research in this area cross different disciplines, spanning geosciences, and various aspects of engineering, material sciences, physics, chemistry, and biology. Among these, soft matter physics has emerged as a fundamental nexus connecting and underpinning many research questions. This perspective article is a multi-voice effort to bring together different views and approaches, questions and insights, from researchers that work in this emerging area, the soft matter physics of the ground beneath our feet. In particular, we identify four major challenges concerned with the dynamics in and of the ground: (I) modeling from the grain scale, (II) near-criticality, (III) bridging scales, and (IV) life. For each challenge, we present a selection of topics by individual authors, providing specific context, recent advances, and open questions. Through this, we seek to provide an overview of the opportunities for the broad Soft Matter community to contribute to the fundamental understanding of the physics of the ground, strive towards a common language, and encourage new collaborations across the broad spectrum of scientists interested in the matter of the Earth's surface.

Characteristic features of strong correlation: lessons from a 3-fermionone-dimensional harmonic trap

Abstract Many quantum phenomena responsible for key applications in material science and quantum
chemistry arise in the strongly correlated regime. This is at the same time, a costly regime for
computer simulations. In the limit of strong correlation analytic solutions exist, but as we move
away from this limit numerical simulation are needed, and accurate quantum solutions do not scale
well with the number of interacting particles. In this work we propose to use few-particle harmonic
traps in combination with twisted light as a quantum emulator to investigate the transition into a
strongly-correlated regime. Using both analytic derivations and numerical simulations we generalize
previous findings on 2 Coulomb interacting fermions trapped in a one-dimensional harmonic trap
to the case of 3 fermions. The 4 signatures of strong correlation we have identified in the one-
dimensional harmonic trap are: i) the ground state density is highly localized around N equilibrium
positions, where N is the number of particles, ii) the symmetric and antisymmetric ground state
wavefunctions become degenerate, iii) the von Neumann entropy grows, iv) the energy spectrum is
fully characterized by N normal modes or less. Our findings describe the low-energy behavior of
electrons in quantum wires and ions in Paul traps. Similar features have also been reported for cold
atoms in optical lattices.

Design and Fabrication of Nano‐Particles with Customized Properties using Self‐Assembly of Block‐Copolymers

AbstractFunctional nanoparticles (NPs) have gained significant attention as promising applications in various fields, including sensor, smart coating, drug delivery, and more. Here, a novel mechanism assisted by machine‐learning workflow is proposed to accurately predict phase diagram of NPs, which elegantly achieves tunability of shapes and internal structures of NPs using self‐assembly of block‐copolymers (BCP). Unlike most of previous studies, onion‐like and mesoporous NPs in neutral environment and hamburger‐like NPs in selective environment are obtained. Such novel phenomena are obtained only by tailoring the topology of a miktoarm star BCP chain architecture without the need for any further treatment. Moreover, it is demonstrated that the BCP chain architecture can be used as a new strategy for tuning the lamellar asymmetry of NPs. It is shown that the asymmetry between A and B lamellae in striped ellipsoidal and onion‐like particles increases as the volume fraction of the A‐block increases, beyond the level reached by linear BCPs. In addition, an extended region of onion‐like structure is found in the phase diagram of A‐selective environment, as well as the emergence of an inverse onion‐like structure in the B‐selective one. The findings provide a valuable insight into the design and fabrication of nanoscale materials with customized properties, opening up new possibilities for advanced applications in sensing, materials science, and beyond.

C─H Activation Enables the Construction of New Bis-Polyaryl Phenylpyridine Ruthenium Complexes: Conjugation and Rigidity Synergistic Effect for Advanced Electrochemiluminescence.

The access to bench-stable organometallic compounds unfolds new chemical space for medicinal and material sciences. In particular, stable organoruthenium compounds with constitutional and stereoisomeric forms for subtle regulation of electrochemiluminescence are intriguing and challenging. Here, coordination of polycyclic aromatic hydrocarbons on (2-phenylpyridine)2(CO)2Ru complex allows access to bis-polyaryl phenylpyridine (BPP) Ruthenium complex through C─H activation strategy and coupling reactions for installation of the functionalities with steric and electronic purposes. The photoluminescence and electrochemiluminescence of BPP Ru complexes are affected by the actual polycyclic aromatic hydrocarbons inherent properties. The anthracene derivatized BPP Ru complex (BPP-Ant) shows the best ECL performance and reveals an enormous ECL quantum efficiency of 1.6-fold higher than the golden standard Ru(bpy)3 2+. The unprecedentedly high efficiency is due to the best compromise between the structural conjugation and molecular rigidity from BPP-Ant providing a providential energy gap that facilitated the feasibility of electron transfer and favored the radiative energy release by experimentally and DFT calculations. Moreover, PL and spooling ECL spectroscopies are used to track and link multiple emission peaks of BPP-Ant at 445, 645, and 845nm to different emissive species. These discoveries will add a new member to the efficient ECL ruthenium complex family and bring more potentials.

Preparation and characterization of poly(butylene adipate‐co‐terephthalate) composites reinforced with acetylated cellulose nanocrystals and nanofibers for enhanced properties

AbstractNanocellulose‐reinforced composites hold great promise for outperforming the original polymer matrix by offering exceptional properties, including outstanding barrier characteristics achieved through the establishment of convoluted pathways for the diffusion of water vapor and gases, as well as superior mechanical and thermal properties. Nevertheless, a significant challenge when employing nanocelluloses as reinforcements in polymers arises from their limited compatibility with the polymer matrix, primarily attributed to the abundance of surface hydroxyl groups.To address this limitation, this study focuses on the acetylation modification of cellulose nanocrystals (CNC) and nanofibers (CNF) to enhance their compatibility with the polymer matrix. Poly(butylene adipate‐co‐terephthalate) (PBAT) nanocomposites were developed by incorporating 1 wt% of both untreated and acetylated cellulose. The process involved the molten state mixing of these components using a twin‐screw mini extruder, conducted at 160°C for 7 min. The efficacy of the acetylation process was verified through Fourier transform infrared spectroscopy (FTIR). Scanning electron microscopy (SEM) analysis confirmed the favorable interaction and dispersion of nanocelluloses within the polymer matrix. Importantly, introducing these fillers did not compromise thermal stability but notably enhanced the hydrophobic characteristics of the samples, as confirmed by contact angle measurements. Dynamic mechanical analyses further demonstrated the compatibility and effective interaction between the polymer and fillers. This research uniquely provides a comparative analysis of the effects of acetylated CNC and CNF within a PBAT matrix, unveiling distinct interactions and performance enhancements. Each type of nanocellulose exhibits its own characteristic ways of bonding and dispersing, leading to varying performance improvements for the material. This comparison elucidates the nuanced roles that these nanocelluloses play in reinforcing composites, offering insights into optimizing their contribution to polymer matrices. By demonstrating the differential impact of CNC and CNF post‐acetylation, our study contributes significantly to the field of polymer science, guiding future applications of nanocellulose‐reinforced materials in advanced engineering and sustainability efforts.