Characterization Of Advanced Materials Research Articles

Transition metal carbo chalcogenides: A novel family of 2D solid lubricants

Two-dimensional (2D) layered materials such as transition metal dichalcogenides (e.g., MoS2, WS2) and MXenes (e.g., Ti3C2Tx), as well as hybrids of these materials are the focus of current research in solid lubrication due to their outstanding performance. Transition metal carbo-chalcogenides (TMCCs), consisting of an MXene core and a TMD-like surface, represent an inherent combination of TMDs and MXenes without the need to construct hybrids out of the individual layers. Due to their layered structure and surface chemistry, favorable tribological properties can be expected from these novel materials. Here, multilayer Ta2S2C and Nb2S2C TMCCs are deposited solely as a powder onto a steel substrate and their tribological properties under linear sliding against different counterbodies, i.e., Al2O3, SiC, 100Cr6, and polytetrafluoroethylene (PTFE) are discussed. Advanced materials characterization techniques are used to detect the presence of TMCCs inside the wear tracks and to reveal their 2D structure within the tribofilm. Finally, density functional theory (DFT) simulations are used to unravel the easy shearability of TMCCs at the nanoscale. The results demonstrate the great potential of this new 2D material family, which also offers many possibilities for defined tuning of the solid-solid interface.

Refractive Index Resolved Imaging Enabled by Terahertz Time-Domain Spectroscopy Ellipsometry

Material characterization in the terahertz range is an interesting topic of research due to its great applications in material science, health monitoring, and security applications. Advances in terahertz generation, detection, and data acquisition have contributed to improved bandwidth, signal power, and signal-to-noise ratio. This enables advanced material characterization methods such as ellipsometry, which has been little explored in the terahertz frequency range, yet. Here, we introduce a comparison between material characterization with terahertz time-domain spectroscopy in transmission geometry and ellipsometry reflection geometry. Terahertz ellipsometry images were taken, showing spatially resolved refractive index estimation in the far field and higher image quality compared to single-polarization imaging.

Towards the Direct Observation of the Local and Electronic Structure Evolution of Positive Electrode Materials by X-Ray Raman Scattering and X-Ray Emission Spectroscopy

The emergence of high brilliance synchrotron sources and the availability of more sophisticated infrastructure have opened doors to advanced material characterization, enabling a profound understanding of new processes and mechanisms in battery research. [1] More specifically, non-resonant Valence-to-Core X-ray emission spectroscopy (VtC-XES) and X-ray Raman scattering (XRS) stand out as complementary photon-in/photon-out X-ray spectroscopy techniques. The unique capability of these techniques lies in their access to soft X-ray edges using a hard X-ray beam, providing comprehensive information on both the electronic and local structure, due to their high sensitivity to local structure and coordination. [2,3] In this work, two different families of materials with different compositions and crystal structures are studied using XES and XRS.The first part focuses on the detailed study of oxygen substituted KVPO4F1-xOx (x = 0, 0.25, 0.5, 0.75, 1) using VtC-XES combined to ab initio theoretical calculations. The average working potential increases with increasing oxygen content by activating the V3+/4+ and V4+/5+ redox couples and replacing the VO4F2 “ionic” entity by {V=O}O5 unit with a highly “covalent” vanadyl-type bond. [4] VtC-XES allowed us to distinguish the contributions of V-F, V-O, and V=O bonds in the local environment, with spectral intensities highly correlated to the F- and O2- anionic composition. In addition, specific spectral evolutions are also associated to the presence of highly covalent V=O bonds, strongly influencing the electrode potential of the material.The second part examines a series of electrochemically obtained LiyNiO2 layered oxide materials (y = 0.02, 0.25, 0.33, 0.5, 0.67, 1.0). VtC-XES performed at the Ni Kβ emission line allows to study the local structure evolution around Ni, whereas XRS spectroscopy at both the O K-edge and Ni L2,3 L-edges gives an access to the interplay between Ni and O through the hybridized states of Ni 3d and O 2p. [5] This work provides a survey on both the cationic and anionic redox processes, and unveils the underlying phenomena related to the charge compensation processes occurring in this material.Overall, we present a comprehensive study of different polyanionic and layered oxide positive electrode materials using complementary X-ray spectroscopic techniques to probe both the local and electronic structures. We demonstrate that they allow to follow and understand evolution and changes in the local structure in materials of intricate crystal structures.AcknowledgementThis work was supported by the DESTINY Marie Skłodowska-Curie Actions COFUND PhD Programme (Grant Agreement #945357) co-funded by the European Union's Horizon2020 research and innovation program and the Synchrotron SOLEIL. ANR is also acknowledged for funding the RS2E network through the STORE-EX Labex Project ANR-10-LABX-76-01. Alistore-ERI network is also acknowledged.

2D Super-Resolution Metrology Based on Superoscillatory Light.

Progress in the semiconductor industry relies on the development of increasingly compact devices consisting of complex geometries made from diverse materials. Precise, label-free, and real-time metrology is needed for the characterization and quality control of such structures in both scientific research and industry. However, optical metrology of 2D sub-wavelength structures with nanometer resolution remains a major challenge. Here, a single-shot and label-free optical metrology approach that determines 2D features of nanostructures, is introduced. Accurate experimental measurements with a random statistical error of 18nm (λ/27) are demonstrated, while simulations suggest that 6nm (λ/81) may be possible. This is far beyond the diffraction limit that affects conventional metrology. This metrology employs neural network processing of images of the 2D nano-objects interacting with a phase singularity of the incident topologically structured superoscillatory light. A comparison between conventional and topologically structured illuminations shows that the presence of a singularity with a giant phase gradient substantially improves the retrieval of object information in such an optical metrology. This non-invasive nano-metrology opens a range of application opportunities for smart manufacturing processes, quality control, and advanced materials characterization.

A crystal plasticity based strain rate dependent model across an ultra-wide range

Numerous studies have investigated the strain rate sensitive behaviors of materials, consistently reporting enhanced stress values and increased dislocation density with rising strain rates. Behind these phenomena lies the intrinsic nature of dislocation activity. In this context, we introduce an analysis method within a crystal-plasticity (CP) framework, incorporating molecular dynamics insights for a comprehensive range of strain rates (7.5 × 10−5/s to 5 × 107/s). This approach offers a refined understanding of strain rate sensitive behaviors, mainly influenced by dislocation movement laws and strain-rate-dependent saturation of dislocation density. We elucidate the impact of deformation loading conditions on Schmidt factors and active slip systems, which are also crucial for understanding variations in SRS. Ultimately, this study underscores the CP method's effectiveness in comprehensive SRS analysis, seamlessly integrating experimental observations with theoretical predictions for advanced material characterization.

Review of Machine Learning Techniques in Soft Tissue Biomechanics and Biomaterials.

Advanced material models and material characterization of soft biological tissues play an essential role in pre-surgical planning for vascular surgeries and transcatheter interventions. Recent advances in heart valve engineering, medical device and patch design are built upon these models. Furthermore, understanding vascular growth and remodeling in native and tissue-engineered vascular biomaterials, as well as designing and testing drugs on soft tissue, are crucial aspects of predictive regenerative medicine. Traditional nonlinear optimization methods and finite element (FE) simulations have served as biomaterial characterization tools combined with soft tissue mechanics and tensile testing for decades. However, results obtained through nonlinear optimization methods are reliable only to a certain extent due to mathematical limitations, and FE simulations may require substantial computing time and resources, which might not be justified for patient-specific simulations. To a significant extent, machine learning (ML) techniques have gained increasing prominence in the field of soft tissue mechanics in recent years, offering notable advantages over conventional methods. This review article presents an in-depth examination of emerging ML algorithms utilized for estimating the mechanical characteristics of soft biological tissues and biomaterials. These algorithms are employed to analyze crucial properties such as stress-strain curves and pressure-volume loops. The focus of the review is on applications in cardiovascular engineering, and the fundamental mathematical basis of each approach is also discussed. The review effort employed two strategies. First, the recent studies of major research groups actively engaged in cardiovascular soft tissue mechanics are compiled, and research papers utilizing ML and deep learning (DL) techniques were included in our review. The second strategy involved a standard keyword search across major databases. This approach provided 11 relevant ML articles, meticulously selected from reputable sources including ScienceDirect, Springer, PubMed, and Google Scholar. The selection process involved using specific keywords such as "machine learning" or "deep learning" in conjunction with "soft biological tissues", "cardiovascular", "patient-specific," "strain energy", "vascular" or "biomaterials". Initially, a total of 25 articles were selected. However, 14 of these articles were excluded as they did not align with the criteria of focusing on biomaterials specifically employed for soft tissue repair and regeneration. As a result, the remaining 11 articles were categorized based on the ML techniques employed and the training data utilized. ML techniques utilized for assessing the mechanical characteristics of soft biological tissues and biomaterials are broadly classified into two categories: standard ML algorithms and physics-informed ML algorithms. The standard ML models are then organized based on their tasks, being grouped into Regression and Classification subcategories. Within these categories, studies employ various supervised learning models, including support vector machines (SVMs), bagged decision trees (BDTs), artificial neural networks (ANNs) or deep neural networks (DNNs), and convolutional neural networks (CNNs). Additionally, the utilization of unsupervised learning approaches, such as autoencoders incorporating principal component analysis (PCA) and/or low-rank approximation (LRA), is based on the specific characteristics of the training data. The training data predominantly consists of three types: experimental mechanical data, including uniaxial or biaxial stress-strain data; synthetic mechanical data generated through non-linear fitting and/or FE simulations; and image data such as 3D second harmonic generation (SHG) images or computed tomography (CT) images. The evaluation of performance for physics-informed ML models primarily relies on the coefficient of determination . In contrast, various metrics and error measures are utilized to assess the performance of standard ML models. Furthermore, our review includes an extensive examination of prevalent biomaterial models that can serve as physical laws for physics-informed ML models. ML models offer an accurate, fast, and reliable approach for evaluating the mechanical characteristics of diseased soft tissue segments and selecting optimal biomaterials for time-critical soft tissue surgeries. Among the various ML models examined in this review, physics-informed neural network models exhibit the capability to forecast the mechanical response of soft biological tissues accurately, even with limited training samples. These models achieve high values ranging from 0.90 to 1.00. This is particularly significant considering the challenges associated with obtaining a large number of living tissue samples for experimental purposes, which can be time-consuming and impractical. Additionally, the review not only discusses the advantages identified in the current literature but also sheds light on the limitations and offers insights into future perspectives.

Melting phenomenon of thermally stratified MHD Powell–Eyring nanofluid with variable porosity past a stretching Riga plate

Abstract Recently, experts have become particularly interested in the treatment of disorders caused by magnesium shortage. Hypomagnesemia is produced by a magnesium deficit in the blood, which is an additional stimulation for different diseases such as vomiting, drowsiness, nausea, loss of appetite, and so on. To compensate for this shortage, magnesium is injected as a nanoparticle in the blood (base fluid). The properties of these magnesium nanoparticles specifically their size, dispersion, and surface characteristics are crucial for optimizing their bioavailability and therapeutic efficacy. Advanced material characterization techniques ensure the stability and enhanced performance of these nanoparticles in the physiological environment. Based on these applications, the current research aims to address magnesium deficiency via Powell–Eyring nanofluid flow distorted by the linearly stretchable sheet in the region of the stagnation point. Linear thermal stratification, viscous dissipation, and Joule heating are used to reveal the heat transport features. The magnetic field is applied to the nanofluid at an angle α to further control the fluid dynamics and nanoparticle behavior. Transformations are used to create a set of dimensionless governing equations. Comparative graphs are used to explain the physical behaviors of temperature and velocity fields, demonstrating how material properties impact fluid dynamics. The well-known convergence method (homotopy analysis method) is used to solve the model by comparing graphs.

An insight into the influence of precipitation phase on the surface quality in diamond turning of an Aluminium alloy

Diamond turning is an effective technology for processing metal mirrors used in photoelectric communications, radar, and other fields. In diamond turning, the precipitated phase is an essential factor that influences the surface quality of the metal mirrors. However, in previous studies, the precipitation phase has typically been handled as a random variable in a surface morphology model to evaluate its influence on the surface roughness, instead of determining the formation mechanism and proposing suppression solutions. In this study, a new phenomenon is observed in the diamond turning of metal mirrors, that is, the micro diamond tool can reduce the protrusion of the precipitated phase under a small feed rate and improve the surface quality. Investigating the turning process using diamond tools with varying tool nose radii at small feed rates (<1 μm/r), the underlying transformation mechanism of the precipitation phase is determined with the advanced material characterization technologies. The growth of the precipitated phase with an increase in the tool nose radius is explained using the energy gradient theory. The results showed that the increased material strain on the machined surface decreased the activation energy of solute diffusion in the material, causing solute accumulation and precipitate phase growth. With a further increase of tool nose radius to around 1000 μm, the β'' phase breaks and rotates. The representative volume element method shows that when undergoing severe plastic deformation, dislocations and grain boundaries quickly aggregate and slide on the precipitated phase, which will lead to the fracture and rotation of β'' phase. These findings provide a theoretical basis for the development of highly smooth mirrors.

Investigations towards nanoscale precise polishing of Nb3Sn thin films for SRF applications

The superconducting radio frequency performance of Nb3Sn is significantly impacted by its near-surface composition and nanostructure. In this study, an innovative polishing technique for Nb3Sn thin films with nanoscale precision is proposed. Systematic angle-resolved X-ray photoelectron spectroscopy (ARXPS) analysis reveal that the mechanism of this polishing process involves the formation of a few nanometer-thick oxide layer on the film's surface, followed by the uniform removal of the oxide layers with HF rinse. It is approximated that the polishing depth achieved in a single cycle can reach several nanometers. Advanced material characterization demonstrates that optimized polishing process effectively eliminates compulsive tin droplets and potential tin-rich phases with poor superconductivity generated on the surface during the preparation of Nb3Sn thin films. Simultaneously, no additional impurity phase will be formed on the surface, and the surface roughness will remain undamaged. Meanwhile, the results of magnetic and electrical analysis show that the superconducting properties of the polished films are not affected. This polishing technique is anticipated to ease the stringent coating conditions for the preparation of high-performance Nb3Sn thin film superconducting cavity, and will be a valuable addition to the post-treatment process of Nb3Sn thin films.

Luminescent Supramolecular Metallogels: Drug Loading and Eu(III) as Structural Probe.

Supramolecular metallogels combine the rheological properties of gels with the color, magnetism, and other properties of metal ions. Lanthanide ions such as Eu(III) can be valuable components of metallogels due to their fascinating luminescence. In this work, we combine Eu(III) and iminodiacetic acid (IDA) into luminescent hydrogels. We investigate the tailoring of the rheological properties of these gels by changes in their metal:ligand ratio. Further, we use the highly sensitive Eu(III) luminescence to obtain information about the chemical structure of the materials. In special, we take advantage of computational calculations to employ an indirect method for structural elucidation, in which the simulated luminescent properties of candidate structures are matched to the experimental data. With this strategy, we can propose molecular structures for different EuIDA gels. We also explore the usage of these gels for the loading of bioactive molecules such as OXA, observing that its aldose reductase activity remains present in the gel. We envision that the findings from this work could inspire the development of luminescent hydrogels with tunable rheology for applications such as 3D printing and imaging-guided drug delivery platforms. Finally, Eu(III) emission-based structural elucidation could be a powerful tool in the characterization of advanced materials.

Preface

43rd International Deep Drawing Research Group (IDDRG) Conference Sustainable Sheet Forming - Circular Economy Melbourne, Australia 1. Introduction The 43rd International Deep Drawing Research Group (IDDRG) conference was held in Melbourne, Australia, on 12–15 March. The conference was co-organised by Deakin University and the Korean Society for Technology of Plasticity. The conference chairs were: Prof. Bernard Rolfe (Deakin University), A/Prof. Matthias Weiss (Deakin University), and Prof Jeong Whan Yoon (KAIST/Deakin University).During IDDRG 2024, all engineering aspects of sheet metal forming were discussed, such as behaviour of workpiece materials, formability, tools, tribology, process robustness, simulation methods and experiments. The general theme of IDDRG 2024 was Sustainable Sheet Forming – Circular Economy. International Deep Drawing Research Group The International Deep Drawing Research Group (IDDRG) was started in the late 1950s as an organization of national groups devoted to the study of sheet metal forming including forming processes, materials, formability issues, tooling, tribology and many other interesting aspects of sheet metal forming research and industrial practices.At the IDDRG, the leading sheet metal specialists and researchers from all over the world gather for fruitful discussions on challenging technical topics in forming technology. The conference is designed to provide an open and inspiring environment for knowledge development, discussions and interactions. Conference topics The theme of the IDDRG 2024 conference was Sustainable Sheet Forming – Circular Economy. In addition to the main theme of the conference, topical sessions with the focus on the challenges and current trends in the field of sheet metal forming, such as: fossil-free steel, sustainable/advanced/smart materials, multi-material concepts/solutions, forming/formability, lightweight structures, digital transformation, digitalization, smart plants, smart press shops, cold and hot forming and trimming (incl. single and multi-step forming and trimming), tribology, simulations (incl. material models, topology optimization, tribology, springback compensation, and geometry assurance), testing, testbeds, springback, shape accuracy, geometry assurance, surface quality, tools and dies, and additive manufacturing.There were six additional mini-symposiums organised by internationally renowned experts held at IDDRG 2024:• 1. Material, Process Monitoring and Quality Control in Roll Forming Processes, organized by Matthias Weiss (Deakin University, Australia);• 2. Forming, Joining and Failure of Fuel Cell and Battery Materials, organized by Michael Pereira (Deakin University, Australia), and Jeong Yoon (KAIST/Deakin University, Korea/Australia);• 3. Machine Learning Driven Forming, organized by Hyunsung Choi (Korea Institute of Materials Science, Korea);• 4. Flexible Metal Forming, organized by Namsu Park (Korea Institute of Industrial Technology, Korea);• 5. Innovative Metal Forming Technology, organized by Professor Richard Yang (Western Sydney University, Australia);• 6. Advanced Materials Characterization, organised by Madhav Baral (University of Kentucky, USA).It was the first time to consider fuel cell and battery cell forming, however, given the global conversion to electric vehicles, it is assumed this forming topic may continue at future IDDRG conferences.List of Keynotes, Venue, Chairs, Organising committee, Scientific Committee, Editors, Number of presentations, published scientific papers/first authors, and participants, Technical/Industrial tours, Acknowledgements and Sponsors and exhibitors are available in this pdf.

XPS Study on the determination of binding energies in high-purity TiO2 nanoparticles

This study focuses on the precise determination of the binding energies of high-purity TiO2 nano-particles, using X-ray photoelectron spectroscopy (XPS). TiO2, an essential ceramic in high-tech sectors such as electronics, aerospace and photocatalysis, was analyzed in its pure state (99.5%) directly as received from the supplier, without previous alterations. XPS spectra revealed typical bond energy peaks for Ti at 458.42 eV and 464.13 eV, corresponding to Ti2p3/2 and Ti2p1/2 levels respectively, with a difference of 5.71 eV, validating the expected electron structure for Ti in TiO2. Additionally, peaks were identified at 457.36 eV and 463.36 eV, attributable to Ti3+, present in a Ti3O5 phase, as confirmed by X-ray diffraction (XRD) analysis. The mid-height width (FWHM) of the main Ti4+ peaks, ranging from 1.08 to 1.87 eV, and 0.96 eV for the Ti3+ peaks, highlights the homogeneity and exceptional purity of the sample. These findings not only corroborate the high quality of TiO2, but also provide a crucial benchmark for the scientific community, facilitating de-tailed comparisons with other samples and encouraging the development of innovative processes and materials. This study underscores the value of the XPS technique in the characterization of advanced ceramic materials and sets a standard for future research.

Electronic Noise Spectroscopy of Quasi-Two-Dimensional Antiferromagnetic Semiconductors.

We investigated low-frequency current fluctuations, i.e., electronic noise, in FePS3 van der Waals layered antiferromagnetic semiconductor. The noise measurements have been used as noise spectroscopy for advanced materials characterization of the charge carrier dynamics affected by spin ordering and trapping states. Owing to the high resistivity of the material, we conducted measurements on vertical device configuration. The measured noise spectra reveal pronounced Lorentzian peaks of two different origins. One peak is observed only near the Néel temperature, and it is attributed to the corresponding magnetic phase transition. The second Lorentzian peak, visible in the entire measured temperature range, has characteristics of the trap-assisted generation-recombination processes similar to those in conventional semiconductors but shows a clear effect of the spin order reconfiguration near the Néel temperature. The obtained results contribute to understanding the electron and spin dynamics in this type of antiferromagnetic semiconductors and demonstrate the potential of electronic noise spectroscopy for advanced materials characterization.

Low‐Carbon‐Footprint Plasma‐Functionalized Coconut‐Coir‐Based Porous Carbon as an Efficient and Sustainable Adsorbent

AbstractHerein, the surface of pyrolyzed coconut coir (PCC) dust has been functionalized using atmospheric plasma under non‐equilibrium conditions without utilizing vacuum chambers. The reactive species present in the plasma such as, molecular ions, electrons, and radicals interact with PCC's surface, leading to the introduction of epoxy, carbonyl and amino functional groups on carbon surface. The resulting plasma functionalized carbon (PFC) material was characterized using several advanced material characterization techniques, including PXRD, FTIR, XPS, N2 BET specific surface area, SEM and TEM imaging techniques, etc. Moreover, it has been used in diverse applications in toxic gas removal (CO2, CO, NOx), fluoride (F−) removal from drinking water and cationic organic dye (methylene blue–MB) removal. The reported method for synthesis of functional carbon introduces a platform technology for low carbon footprint, green synthesis of advanced functional materials.

Shear strength recovery of sand with self-healing polymeric capsules

Self-healing approaches are increasingly being explored in various fields as a potential method to recover damaged material properties. By self-recovering without external intervention, self-healing techniques emerge as a potential solution to arrest or prevent the development of large strains problems in soils (e.g., landslides) and other ground effects that influence the serviceability of structures (e.g., differential settlement). In this study, a microcapsule-based self-healing sand was developed, and its performance during mixing and compaction, shearing, and recovery of shear strength was demonstrated. The cargo used for sand improvement, a hardening oil, tung oil, was encapsulated in calcium alginate capsules by the ionic gelation method. The surface properties, internal structure, thermal stability and molecular structure of the capsules were evaluated by advanced material characterization techniques. The survivability of capsules during mixing and compaction was assessed by measuring the content of tung oil released into the sand, while their influence on sand shear strength and its recovery was assessed with shear box tests. The results showed that the capsules could rupture due to movement of the sand particles, releasing the tung oil cargo, leading to its hardening and minimizing its strain-softening response and enhancing up to 76% of the sand shear strength (at a normal stress of 10 kPa and capsules content of 4%). This study demonstrates the potential of a capsules-based self-healing system to provide ‘smart’ autonomous soil strength recovery and thus with potential to actively control the large strain behavior of soils.

Welcome to the MatSci Express

Welcome to the inaugural issue of MatSci Express! We are delighted to present a diverse array of articles that encapsulate the forefront of materials science research. From comprehensive reviews to original research articles, each contribution represents a significant advancement in the field. Our journal serves as a dynamic platform for scientists, researchers, and scholars to share their discoveries and insights, fostering collaboration and innovation. In this edition, you will find insightful investigations into topics ranging from environmental remediation to advanced materials synthesis and characterization. These articles highlight the interdisciplinary nature of materials science and underscore its critical role in addressing global challenges and driving technological progress. As we embark on this exciting journey, we extend our sincere appreciation to the authors for their valuable contributions and to the reviewers for their meticulous evaluations. We invite you to immerse yourself in the cutting-edge research presented in this inaugural issue and join us in advancing the frontiers of materials science.

Microstructure Study on Very High Cycle Fatigue of an Additively Manufactured Aluminium Alloy via Advanced Characterization Methods

The engineering application of additively manufactured (AM) metallic materials is quite limited by their fatigue behaviors, which are very inconsistent with that of conventionally wrought or cast ones. Here, based on advanced material characterization techniques, such as focused ion beam (FIB), scanning electron microscopy (SEM), and transmission electron microscopy (TEM), the microstructures underneath fracture surfaces were thoroughly investigated in an AM aluminum (AlSi10Mg) alloy with horizontal and vertical building orientation enduring very high cycle fatigue (VHCF) loading under the stress ratios R = −1, 0, and 0.5. Two VHCF failure specimens A and B were representatively selected to further examine SEM and TEM sample preparation via FIB milling. Specimen A was horizontally printed and failed at R = −1; specimen B was vertically printed and failed at R = 0. TEM samples A1 and B1 were lifted from locations near the crack initiation sites on the fracture surfaces of specimens A and B; The locations of TEM samples A2 and B2 kept away from the crack origin sites but still within the “fish-eye” region of crack steady growth. TEM observations show that there was no characteristic microstructure induced by VHCF in different oriented specimens and under various R values.

Advances in Small Angle Neutron Scattering on Polysaccharide Materials.

Polysaccharide materials and biomaterials gain the focus of intense research owing to their great versatility in chemical structures and modification possibilities, as well as their biocompatibility, degradability, and sustainability features. This review focuses on the recent advances in the application of SANS on polysaccharide systems covering a broad range of materials such as nanoparticulate assemblies, hydrogels, nanocomposites, and plant-originating nanostructured systems. It motivates the use of SANS in its full potential by demonstrating the features of contrast variation and contrast matching methods and by reporting the methodologies for data analysis and interpretation. As these soft matter systems may be organized in multiple length scales depending on the interactions and chemical bonds between their components, SANS offers exceptional and unique opportunities for advanced characterization and optimization of new nanostructured polysaccharide materials.

Hole formation mechanisms in double-sided laser drilling of Ti6Al4V-C/SiC stacked materials

This is the first investigation on the potential of doubled-sided laser drilling for hole making in Ti6Al4V-C/SiC, a hard-to-machine stacked structure that holds critical significance in high-temperature structural applications. While previous literature reported laser drilling-induced microstructural change in Ti6Al4V and C/SiC single sheets respectively, a comprehensive understanding of the melt pool formation process and its influence on hole morphology/quality is still lacking. The complexity increases when Ti6Al4V is coupled with C/SiC in a stacked structure, highlighting a significant yet understudied knowledge gap in understanding the intricate material interactions at the interface. Here we present a multifaceted study combining novel computational fluid dynamics (CFD) modeling approach with ablation mechanism investigation through advanced microstructure analysis. A hybrid CFD modeling strategy integrating volume of fluid method and level set method has been developed to successfully simulate the evolution of hole shape, recast layer formation, and melt pool flow behaviour in Ti6Al4V-C/SiC. This model provides comprehensive insights into molten pool dynamics under various laser drilling conditions, fostering a fundamental understanding of the mechanism that drives recast layer formation and influencing surface quality. To validate our modeling outcomes, extensive experimental investigations were undertaken across a range of laser drilling conditions. Whenever possible, samples were analysed using advanced materials characterization techniques, including scanning electron microscopy, energy dispersive spectroscopy, and electron backscatter diffraction, to elucidate the microstructural evolution within the recast layer and the formation of the heat-affected zone. Whenever possible, the results were also compared with single side drilled Ti6Al4V-C/SiC stacks, Ti6Al4V or C/SiC single sheets (i.e., the benchmark references). Our experimental findings align well with the modeling results, systematically illustrating that doubled-sided laser drilling of the stacked structure outperforms the conventional single-sided laser drilling in terms of hole morphology, surface roughness, and reduced recast layer formation. Despite these promising results, the proposed technology is currently limited to laboratory scales and lacks capacity in manufacturing certain parts with complex geometry. Future outlook has therefore been proposed with potential solution to address these challenges and enable real world applications in future.

Short-wave Infrared Photoluminescence Lifetime Mapping of Rare-Earth Doped Nanoparticles Using All-Optical Streak Imaging.

The short-wave infrared (SWIR) photoluminescence lifetimes of rare-earth doped nanoparticles (RENPs) have found diverse applications in fundamental and applied research. Despite dazzling progress in the novel design and synthesis of RENPs with attractive optical properties, existing optical systems for SWIR photoluminescence lifetime imaging are still considerably restricted by inefficient photon detection, limited imaging speed, and low sensitivity. To overcome these challenges, SWIR photoluminescence lifetime imaging microscopy using an all-optical streak camera (PLIMASC) is developed. Synergizing scanning optics and a high-sensitivity InGaAs CMOS camera, SWIR-PLIMASC has a 1D imaging speed of up to 138.9kHz in the spectral range of 900-1700nm, which quantifies the photoluminescence lifetime of RENPs in a single shot. A 2D photoluminescence lifetime map can be acquired by 1D scanning of the sample. To showcase the power of SWIR-PLIMASC, a series of core-shell RENPs with distinct SWIR photoluminescence lifetimes is synthesized. In particular, using Er3+ -doped RENPs, SWIR-PLIMASC enables multiplexed anti-counterfeiting. Leveraging Ho3+ -doped RENPs as temperature indicators, this system is applied to SWIR photoluminescence lifetime-based thermometry. Opening up a new avenue for efficient SWIR photoluminescence lifetime mapping, this work is envisaged to contribute to advanced materials characterization, information science, and biomedicine.