A compositionally graded alloy, transitioning from Ti–6Al–4V to Ti–35Nb–7Zr–5Ta, with nine intermediate compositions, was fabricated using laser-directed energy deposition, as a candidate system for stiffness-matched biomedical implants. This study focuses on microstructural evolution across layers and interfaces, while reducing the effects of thermal history - an aspect largely unexplored in prior investigations on graded alloys. Microstructural characterization of the as-deposited condition revealed a gradual transition from HCP α + BCC β phases near the Ti–6Al–4V end to a fully β phase near the Ti–35Nb–7Zr–5Ta end, consistent with XRD analysis and CALPHAD predictions. Post-processing β-solutionization and quenching led to a partial dissolution of the cellular structure, and a phase evolution from martensitic α′ to α” and eventually to a fully β microstructure, across the gradient from Ti–6Al–4V to Ti–35Nb–7Zr–5Ta. EBSD analysis indicated an absence of strong crystallographic texture, with β grains elongated along the build direction. Elastic modulus measurements exhibited a decreasing trend from 0% to 80% TNZT, followed by an increase toward the 100% TNZT composition. TEM investigations revealed the presence of metastable ω and O’ phases in the 80% and 100% TNZT layers, which may be the potential reason for this non-uniform trend in modulus.

Annealing heat treatments lead to microstructural transformation, but such transformations have rarely been observed in situ. Spatially Resolved Acoustic Spectroscopy is a non-destructive technique for imaging microstructure, which is fast enough to image dynamic changes in the sample. This study demonstrates the in situ monitoring of the annealing process that includes the recovery, recrystallisation and grain growth. The microstructural changes are observed by measuring the surface acoustic wave velocities, allowing the grain evolution to be monitored.

This paper presents a method for imaging defects within 3D curved metallic components using laser-induced phased array (LIPA). Beyond extending LIPA to curved samples, a strategy for accelerating data acquisition is introduced by selecting the most favourable generation-detection locations for a given inspection volume, while reducing false positives by mapping geometry-induced artifacts and regions of low-sensitivity ('dead-zones'). To move beyond planar or near-planar geometries, a system using two robot arms is introduced to position laser generation and detection points on the sample surface in near-arbitrary fashion, limited only by access. Analytical predictions of detection sensitivity are combined with finite element modelling to map sensitivity throughout the component volume. This mapping guides the robots in selecting optimal pairs to maximise signal-to-noise within the inspection zone. The approach is experimentally validated on cylindrical and semi-cylindrical samples with side-drilled defects. Using only optimal data pairs, 75%-85% of total measurements is eliminated, achieving four-to sixfold speed-ups. The effect of surface waviness is also modelled, predicting enhanced detection by partially removing 'dead-zones', at the cost of requiring surface measurement. These combined advances mark a significant step towards in the non-destructive evaluation of complex real-world components.

The ability to manipulate elastic waves and achieve efficient mode conversion is important for many applications including energy harvesting, vibration mitigation and elastic wave control. In this paper, we present a novel metamaterial-based wave mode conversion device that enables the conversion of longitudinal-to-shear waves (and vice versa) at normal incidence. The devices achieve this by rotating the direction of polarisation (i.e. the motion vector) of the longitudinal waves to match the (normally orthogonal) polarisation of shear waves. Previously we have demonstrated mode conversion by adjusting the spatial-temporal distribution of the incident wave amplitude, but this approach cannot convert between modes with orthogonal motion vectors. Here we demonstrate conversion between orthogonal motion vectors without changing the spatial-temporal distribution of the field. The devices presented here reorient the direction of motion by coupling the waves into thin waveguides or "elastic wave pipes" such that a single mode is supported. The pipes are curved, and the motion vectors of the guide waves are thus rotated as the wave follows the pipe. Fabrication of these geometrically complex structures has recently been made practical through additive manufacturing allowing devices to be built that operate in the megahertz frequency range. We present the design methodology, finite element simulations and experimental demonstration of broadband mode conversion.

Meteorites provide access to information on the formation and evolution of planetary bodies which is otherwise difficult to study. The unique nature of these samples and their relative scarcity means that non-destructive analysis techniques are needed to study their properties. This paper uses the laser ultrasound technique spatially resolved acoustic spectroscopy to non-destructively determine both the crystal orientation and the single crystal elastic constants (Cif) of a sample of the Gibeon meteorite. There are no published values to directly compare the results of this study, as non-destructive measurements of the single crystal elasticity on granular material have not been possible. Therefore, comparisons with theoretical values for man-made iron-nickel alloys are given showing the Cif values are in the expected range. There are studies providing bulk elastic properties of meteorites, and so calculated bulk properties derived from the single crystal elasticity measurements are compared and also agree well.

Advances in artificial intelligence (AI) show significant promise in multiscale modeling and biomedical informatics, particularly in the analysis of phonon microscopy (high-frequency ultrasound) data for cancer detection. This study addresses critical issues in data engineering for time-resolved phonon microscopy of biomedical samples by tackling the ‘batch effect,’ which arises from unavoidable technical variations between experiments, creating confounding variables that AI models may inadvertently learn. We present a multi-task conditional neural network framework that simultaneously achieves inter-batch calibration by removing confounding variables and accurate cell classification from time-resolved phonon-derived signals. We validate our approach by training and validating on different experimental batches, achieving a balanced precision of 89.22% and an average cross-validated precision of 89.07% for classifying background, healthy and cancerous regions. Furthermore, our model enables reconstruction of denoised images, which enable the physical interpretation of salient features indicative of disease states, such as sound velocity, sound attenuation, and cell adhesion to substrates. This work demonstrates the potential of AI methodologies in improving health outcomes and advancing cancer-informatics platforms.

Elastic waves are important in many application areas. Their manipulation and coupling between different acoustic modes is important and presents a considerable challenge that offers to unlock the flexibility in wave transport required for efficient energy harvesting and vibration mitigation devices. In this paper, we present a new class of metamaterial conversion devices consisting of arrays of "acoustic pipes" that can arbitrarily convert between different acoustic wavemodes. These pipes are used to match the modal patterns and phases between the two different elastic waves. The technique is fairly general and can be used to match any acoustic mode to any other acoustic mode provided an appropriate geometry can be formed. Until recently the complexity of the geometries required has made the physical realisation of practical devices difficult because of the limitations of conventional fabrication processes but here we demonstrate practical devices made using additive manufacturing which can easily produce the complex topographies required for elastic waves around the MHz frequency region.

Elastic waves are important in many application areas. Their manipulation and coupling between different acoustic modes is important and presents a considerable challenge that offers to unlock the flexibility in wave transport required for efficient energy harvesting and vibration mitigation devices.In this paper, we present a new class of metamaterial conversion devices consisting of arrays of “acoustic pipes” that can arbitrarily convert between different acoustic wavemodes. These pipes are used to match the modal patterns and phases between the two different elastic waves. The technique is fairly general and can be used to match any acoustic mode to any other acoustic mode provided an appropriate geometry can be formed.Until recently the complexity of the geometries required has made the physical realisation of practical devices difficult because of the limitations of conventional fabrication processes but here we demonstrate practical devices made using additive manufacturing which can easily produce the complex topographies required for elastic waves around the MHz frequency region.

In this paper, we demonstrate for the first time the focusing of gigahertz coherent phonon pulses propagating in water using picosecond ultrasonics and Brillouin light scattering. We achieve this by using planar Fresnel zone plate and concave lenses with different focal lengths. Pump light illuminating the optoacoustic lens generates a focusing acoustic field, and Brillouin scattered probe light allows the acoustic field to be continuously monitored over time. Agreement of the experiment with a numerical model suggests that we can generate a focused acoustic beam down to ∼250 nm. A clear focusing effect is observed experimentally as a modulation of the envelope of the time-resolved Brillouin scattering (TRBS) signal. These findings are a crucial step toward their application in high-resolution acoustic microscopy. This work experimentally demonstrates a method to narrow the lateral size of picosecond laser-generated phonon fields in an aqueous environment, making it well-suited for 3D imaging applications in biological systems using TRBS.

Rapid measurement of crystal orientation is critical in the materials discovery process as it facilitates real-time decision-making and quality control. Acoustic inspection methods rapidly characterise microstructure without the need for extensive infrastructure or expense - the laser ultrasonic method known as Spatially Resolved Acoustic Spectroscopy (SRAS) has been developed with this intent and accurately characterises crystal orientation by leveraging a combination of forward modelling and an exhaustive brute force process to obtain the best- fit orientation. While effective, this method is computationally demanding and time-intensive. We introduce a novel approach that utilises neural networks to classify measured acoustic signals into orientation planes to significantly expedite the characterisation process and demonstrate classification on real-world Inconel 617 and CMX4 specimens. A reduction in the orientation determination time from around 10 hours (brute force search) down to 15 seconds (neural network) was achieved while exhibiting an average plane angle difference of between 5.3 degrees and 13.8 degrees.

This abstract describes a potential method to improve the lateral resolution of Phonon microscopy, a novel noninvasive elasticity imaging microscopy for 3D cell imaging by measuring the time-resolved Brillouin scattering signal. While this technique provides sub-optical axial resolution, the lateral resolution is limited by the optical system that generates the coherent phonon fields. To overcome this limitation, the authors suggest using novel optoacoustic lenses working in GHz frequencies to focus the laser generated coherent phonon fields and thus obtain true acoustic resolution in both axial and lateral dimensions. These lenses can be fabricated at the nanoscale and can also be compatible with ultrasonic endoscopic imaging systems in further applications.

Advances in artificial intelligence (AI) show great potential in revealing underlying information from phonon microscopy (high-frequency ultrasound) data to identify cancerous cells. However, this technology suffers from the 'batch effect' that comes from unavoidable technical variations between each experiment, creating confounding variables that the AI model may inadvertently learn. We therefore present a multi-task conditional neural network framework to simultaneously achieve inter-batch calibration, by removing confounding variables, and accurate cell classification of time-resolved phonon-derived signals. We validate our approach by training and validating on different experimental batches, achieving a balanced precision of 89.22 cross-validated precision of 89.07 cancerous regions. Classification can be performed in 0.5 seconds with only simple prior batch information required for multiple batch corrections. Further, we extend our model to reconstruct denoised signals, enabling physical interpretation of salient features indicating disease state including sound velocity, sound attenuation and cell-adhesion to substrate.

This report presents an optical fibre-based endo-microscopic imaging tool that simultaneously measures the topographic profile and 3D viscoelastic properties of biological specimens through the phenomenon of time-resolved Brillouin scattering. This uses the intrinsic viscoelasticity of the specimen as a contrast mechanism without fluorescent tags or photoacoustic contrast mechanisms. We demonstrate 2 μm lateral resolution and 320 nm axial resolution for the 3D imaging of biological cells and Caenorhabditis elegans larvae. This has enabled the first ever 3D stiffness imaging and characterisation of the C. elegans larva cuticle in-situ. A label-free, subcellular resolution, and endoscopic compatible technique that reveals structural biologically-relevant material properties of tissue could pave the way toward in-vivo elasticity-based diagnostics down to the single cell level.

Grapevine powdery mildew resistance is a key target for grape breeders and grape growers worldwide. The driver of the USDA-NIFA-SCRI VitisGen3 project is completing the pipeline from germplasm identification to QTL to candidate gene characterization to new cultivars to vineyards to consumers. This is a common thread across such projects internationally. We will discuss how our objectives and approaches leverage big data to advance this initiative, starting with genomics and computer vision phenotyping for gene discovery and genetic improvement. To manage and maintain resistances for long-term sustainability, growers will be trained through our nation-wide extension and outreach plan. Ultimately, consumers drive adoption of new varieties, and our socioeconomic research using eye-tracking will be briefly described. Across this multi-disciplinary research effort, big data presents opportunities, challenges, and lessons.

There is a consensus about the strong correlation between the elasticity of cells and tissue and their normal, dysplastic, and cancerous states. However, developments in cell mechanics have not seen significant progress in clinical applications. In this work, we explore the possibility of using phonon acoustics for this purpose. We used phonon microscopy to obtain a measure of the elastic properties between cancerous and normal breast cells. Utilising the raw time-resolved phonon-derived data (300 k individual inputs), we employed a deep learning technique to differentiate between MDA-MB-231 and MCF10a cell lines. We achieved a 93% accuracy using a single phonon measurement in a volume of approximately 2.5 μm 3 . We also investigated means for classification based on a physical model that suggest the presence of unidentified mechanical markers. We have successfully created a compact sensor design as a proof of principle, demonstrating its compatibility for use with needles and endoscopes, opening up exciting possibilities for future applications.

Microrheology, the study of fluids on micron length-scales, promises to reveal insights into cellular biology, including mechanical biomarkers of disease and the interplay between biomechanics and cellular function. Here a minimally-invasive passive microrheology technique is applied to individual living cells by chemically binding a bead to the surface of a cell, and observing the mean squared displacement of the bead at timescales ranging from milliseconds to 100s of seconds. Measurements are repeated over the course of hours, and presented alongside novel analysis to quantify changes in the cells' low-frequency elastic modulus and the cell's dynamics over the time window from around 0.01s to 10s. An analogy to optical trapping allows verification of the invariant viscosity of HeLa S3 cells under control conditions and after cytoskeletal disruption. Stiffening of the cell is observed during cytoskeletal rearrangement in the control case, and cell softening when the actin cytoskeleton is disrupted by Latrunculin B. These data correlate with conventional understanding that integrin binding and recruitment triggers cytoskeletal rearrangement. This is, to our knowledge, the first time that cell stiffening has been measured during focal adhesion maturation, and the longest time over which such stiffening has been quantified by any means.

The microstructure of a material defines many of its mechanical properties. Tracking the microstructure of parts during their manufacturing is needed to ensure the designed performance can be obtained, especially for additively manufactured parts. Measuring the microstructure non-destructively on real parts is challenging for optical techniques such as laser ultrasound, as the optically rough surface impacts the ability to generate and detect acoustic waves. Spatially resolved acoustic spectroscopy can be used to measure the microstructure, and this paper presents the capability on a range of surface finishes. We discuss how to describe ’roughness’ and how this influences the measurements. We demonstrate that measurements can be made on surfaces with Ra up to 28 μm for a selection of roughness comparators. Velocity images on a range of real surface finishes, including machined, etched, and additively manufactured finishes in an as-deposited state, are presented. We conclude that the Ra is a poor descriptor for the ability to perform measurements as the correlation length of the roughness has a large impact on the ability to detected the surface waves. Despite this issue, a wide range of real industrially relevant surface conditions can be measured.