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Pressure-induced phase transformation in iron and its alloys is a classic research topic in solid-state physics, material science, and geophysics. The crystal structure of iron undergoes a phase transformation at a hydrostatic pressure of 13 GPa, changing from a body-centered cubic system to a hexagonal close-packed system. Although extensive research has been carried out on the transformation in iron by using molecular dynamics simulations, there is very limited literature that focuses on the contribution of parent phase orientations, system size, and impurities to the phase evolution. In this work, classic molecular dynamics simulations have been employed to investigate the effects of system size, lattice orientation, and impurity concentration on the pressure-induced phase transformation of iron and iron alloys for the first time. Our results show that the lattice orientation has a strong influence on the phase transition behavior, while the influence of carbon is small. The phase transition is slightly delayed with increasing carbon content, whereas the transition pressure increases from [001] to [011] to [111] orientation. The amount of twinning and stacking faults highly depends on the orientation. It is easiest for solitary waves to travel through [111] lattice orientation. The addition of carbon has a slow-down effect on shock velocities, and this effect increases with carbon content and lattice orientation of the samples from [001] to [011] to [111]. [ABSTRACT FROM AUTHOR]

In a recent work, we have briefly introduced a new structural index for water that, unlike previous indicators, was devised specifically for generic contexts beyond bulk conditions, making it suitable for hydration and nanoconfinement settings. In this work, we shall study this metric in detail, demonstrating its ability to reveal the existence of a fine-tuned interplay between the local structure and energetics in liquid water. This molecular principle enables the establishment of an extended hydrogen bond network, while simultaneously allowing for the existence of network defects by compensating for uncoordinated sites. By studying different water models and different temperatures encompassing both the normal liquid and the supercooled regime, this molecular mechanism will be shown to underlie the two-state behavior of bulk water. In addition, by studying functionalized self-assembled monolayers and diverse graphene-like surfaces, we shall show that this principle is also operative at hydration and nanoconfinement conditions, thus generalizing the validity of the two-liquid scenario of water to these contexts. This approach will allow us to define conditions for wettability, providing an accurate measure of hydrophobicity and a reliable predictor of filling and drying transitions. Hence, it might open the possibility of elucidating the active role of water in the broad fields of biophysics and materials science. As a preliminary step, we shall study the hydration structure and hydrophilicity of graphene-like systems (parallel graphene sheets and carbon nanotubes) as a function of the confinement dimensionality. [ABSTRACT FROM AUTHOR]

Embodied emissions from the production of building materials account for 17% of Chinas carbon dioxide (CO2) emissions and are important to focus on as China aims to achieve its carbon neutrality goals. However, there is a lack of systematic assessments on embodied emissions reduction potential of building materials that consider both the heterogeneous industrial characteristics as well as the Chinese buildings sector context. Here, we developed an integrated model that combines future demand of building materials in China with the strategies to reduce CO2 emissions associated with their production, using, and recycling. We found that measures to improve material efficiency in the value-chain has the largest CO2 mitigation potential before 2030 in both Low Carbon and Carbon Neutrality Scenarios, and continues to be significant through 2060. Policies to accelerate material efficiency practices, such as incorporating embodied emissions in building codes and conducting robust research, development, and demonstration (RD&D) in carbon removal are critical.

The enigmatic mechanism underlying unconventional high-temperature superconductivity, especially the role of lattice dynamics, has remained a subject of debate. Theoretical insights have long been hindered due to the lack of an accurate first-principles description of the lattice dynamics of cuprates. Recently, using the r2SCAN meta-generalized gradient approximation (meta-GGA) functional, we have been able to achieve accurate phonon spectra of an insulating cuprate YBa2Cu3O6 and discover significant magnetoelastic coupling in experimentally interesting Cu–O bond stretching optical modes [Ning et al., Phys. Rev. B 107, 045126 (2023)]. We extend this work by comparing Perdew–Burke–Ernzerhof and r2SCAN performances with corrections from the on-site Hubbard U and the D4 van der Waals (vdW) methods, aiming at further understanding on both the materials science side and the density functional side. We demonstrate the importance of vdW and self-interaction corrections for accurate first-principles YBa2Cu3O6 lattice dynamics. Since r2SCAN by itself partially accounts for these effects, the good performance of r2SCAN is now more fully explained. In addition, the performances of the Tao–Mo series of meta-GGAs, which are constructed in a different way from the strongly constrained and appropriately normed (SCAN) meta-GGA and its revised version r2SCAN, are also compared and discussed. [ABSTRACT FROM AUTHOR]

Density functional theory (DFT) stands as a cornerstone method in computational quantum chemistry and materials science due to its remarkable versatility and scalability. Yet, it suffers from limitations in accuracy, particularly when dealing with strongly correlated systems. To address these shortcomings, recent work has begun to explore how machine learning can expand the capabilities of DFT: an endeavor with many open questions and technical challenges. In this work, we present GradDFT a fully differentiable JAX-based DFT library, enabling quick prototyping and experimentation with machine learning-enhanced exchange–correlation energy functionals. GradDFT employs a pioneering parametrization of exchange–correlation functionals constructed using a weighted sum of energy densities, where the weights are determined using neural networks. Moreover, GradDFT encompasses a comprehensive suite of auxiliary functions, notably featuring a just-in-time compilable and fully differentiable self-consistent iterative procedure. To support training and benchmarking efforts, we additionally compile a curated dataset of experimental dissociation energies of dimers, half of which contain transition metal atoms characterized by strong electronic correlations. The software library is tested against experimental results to study the generalization capabilities of a neural functional across potential energy surfaces and atomic species, as well as the effect of training data noise on the resulting model accuracy. [ABSTRACT FROM AUTHOR]

The design of metallic glasses (MGs) with good properties is one of the long-standing bottlenecks in materials science and engineering, which has been relying mostly on far less efficient traditional trial-and-error methods. Even the currently popular machine learning-based forward designs, which use manual input to navigate high dimensional compositional space, often become inefficient with the increasing compositional complexity in MGs. Here, we developed an inverse design machine learning model, leveraging the variational autoencoder (VAE), to directly generate the MGs with good glass-forming ability (GFA). We demonstrate that our VAE with the property prediction model is not only an expressive generative model but also able to do accurate property prediction. Our model allows us to automatically generate novel MG compositions by performing simple operations in the latent space. After randomly generating 3000MG compositions using the model, a detailed analysis of four typical metallic alloys shows that unreported MG compositions with better glass-forming ability can be predicted. Moreover, our model facilitates the use of powerful optimization algorithms to efficiently guide the search for MGs with good GFA in the latent space. We believe that this is an efficient way to discover MGs with excellent properties. [ABSTRACT FROM AUTHOR]

In this study, we observed natural methane (CH4) hydrate sediments, which are a type of unconventional natural gas resources, using x-ray computed tomography (CT). Because CH4 hydrates are formed by hydrogen bonding of water molecules with CH4, material decomposition becomes challenging when CH4 hydrates coexist with liquid or solid water in natural sediments. Tri-contrast (absorption, refraction, and scattering) imaging was performed via diffraction enhanced x-ray CT optics using monochromatic synchrotron x rays. The quantitative characterization of the contrast changes successfully enabled the decomposition of CH4 hydrates coexisting with frozen seawater (ice) in natural sediments obtained from the Okhotsk Sea. This study reveals complementary structural information about the microtexture and spatial relation among CH4 hydrates, ice, and pores by utilizing the distinct physical properties of x rays when passing through the materials. These results highlight the exceptional capabilities of high-resolution multicontrast x-ray tomography in materials science and geoscience applications. [ABSTRACT FROM AUTHOR]

The dynamics of a soft particle suspended in a viscous fluid can be changed by the presence of an elastic boundary. Understanding the mechanisms and dynamics of soft–soft surface interactions can provide valuable insights into many important research fields, including biomedical engineering, soft robotics development, and materials science. This work investigates the anomalous transport properties of a soft nanoparticle near a visco-elastic interface, where the particle consists of a polymer assembly in the form of a micelle and the interface is represented by a lipid bilayer membrane. Mesoscopic simulations using a dissipative particle dynamics model are performed to examine the impact of micelle's proximity to the membrane on its Brownian motion. Two different sizes are considered, which correspond to ≈ 10 − 20 n m in physical units. The wavelengths typically seen by the largest micelle fall within the range of wavenumbers where the Helfrich model captures fairly well the bilayer mechanical properties. Several independent simulations allowed us to compute the micelle trajectories during an observation time smaller than the diffusive time scale (whose order of magnitude is similar to the membrane relaxation time of the largest wavelengths), this time scale being hardly accessible by experiments. From the probability density function of the micelle normal position with respect to the membrane, it is observed that the position remains close to the starting position during ≈ 0.05 τ d (where τd corresponds to the diffusion time), which allowed us to compare the negative excess of mean-square displacement (MSD) to existing theories. In that time range, the MSD exhibits different behaviors along parallel and perpendicular directions. When the micelle is sufficiently close to the bilayer (its initial distance from the bilayer equals approximately twice its gyration radius), the micelle motion becomes quickly subdiffusive in the normal direction. Moreover, the temporal evolution of the micelle MSD excess in the perpendicular direction follows that of a nanoparticle near an elastic membrane. However, in the parallel direction, the MSD excess is rather similar to that of a nanoparticle near a liquid interface. [ABSTRACT FROM AUTHOR]

Low-crosslinked polymer networks have recently been found to behave auxetically when subjected to small tensions, that is, their Poisson's ratio ν becomes negative. In addition, for specific state points, numerical simulations revealed that diamond-like networks reach the limit of mechanical stability, exhibiting values of ν = −1, a condition that we define as hyper-auxeticity. This behavior is interesting per se for its consequences in materials science but is also appealing for fundamental physics because the mechanical instability is accompanied by evidence of criticality. In this work, we deepen our understanding of this phenomenon by performing a large set of equilibrium and stress–strain simulations in combination with phenomenological elasticity theory. The two approaches are found to be in good agreement, confirming the above results. We also extend our investigations to disordered polymer networks and find that the hyper-auxetic behavior also holds in this case, still manifesting a similar critical-like behavior as in the diamond one. Finally, we highlight the role of the number density, which is found to be a relevant control parameter determining the elastic properties of the system. The validity of the results under disordered conditions paves the way for an experimental investigation of this phenomenon in real systems, such as hydrogels. [ABSTRACT FROM AUTHOR]

Interfacial water is ubiquitous on Earth, playing a crucial role in biology, chemistry, physics, materials science, and environmental science. Multiscale, hierarchical water motions on the surface of different materials under different conditions (temperature, hydration extent, pressure, etc.) and the coupling of this motion with the substrate/solute dynamics and the influence of these couplings on the material functions are complex, long lasting, interdisciplinary research topics. We herein focus on the coupling between the picosecond dynamical onset of substrates and their surface water at temperatures lower than the freezing point (273 K) and discuss the recent progress in the study of its molecular mechanisms. [ABSTRACT FROM AUTHOR]

The characteristic x-ray emission direction of a material indicates the direction of the bonding orbitals and spatial symmetry of the electron orbitals. Accordingly, the intensity of x-ray emission, which varies with the direction of emission and crystal orientation, provides crucial information regarding anisotropic electronic structures. This study utilized angle-resolved soft x-ray emission spectroscopy (SXES) on a layered material, NaAlSi, to ascertain the spatial distribution of the valence electrons. Distinct alterations in the spectral intensity distributions were observed in the Al–L2,3 and Si–L2,3 spectra with respect to the emission angle. To interpret the anisotropic SXES spectra, the spatial distribution of each valence electronic state was simulated using first-principle calculations. Although the anisotropic emission intensity could not explain the symmetry of the spatial distributions of the isolated s and d atomic orbitals, the anisotropy of the SXES spectra could be interpreted as the spatial distribution of these orbitals when hybridized with p orbitals. Furthermore, the spectral structure corresponding to the electronic states near the Fermi level reflected the characteristics of the d orbitals. Therefore, angle-resolved SXES measurements can effectively discern the spatial distribution of hybridized electron orbitals with specific energy levels, which could enhance techniques related to electron distribution analysis, with potential applications in material science and electronic structure characterization. [ABSTRACT FROM AUTHOR]

Realizing an oxide thin film with proper stoichiometry is one of the most challenging objects in materials science. Owing to the growth dynamics as well as kinetics, the physical properties of thin films often differ from their bulk counterparts. Here, we report pulsed laser-deposited thin films of LaBO3 (B = Mn, Fe, Co) grown on a SrTiO3 (001) substrate under various thermodynamic conditions. Structural, magnetic, and optical studies have been carried out. The x-ray diffraction study confirms that an appropriate choice of growth thermodynamics may help one to realize epitaxially grown films on the SrTiO3 substrate with out-of-plane lattice parameters 3.976, 3.984, and 3.825 Å for LaMnO3 (LMO), LaFeO3 (LFO), and LaCoO3 (LCO), respectively. A mixed valence state of Mn2+, 3+, 4+ for LMO, a Fe3+ state for LFO, and a mixed state of Co2+, 3+ for LCO have been confirmed by x-ray photoelectron spectroscopy, which is in good agreement with the Ellingham diagram. The optical study showed a bandgap of 1.2, 2.5, and 1.5 eV for LMO, LFO, and LCO, respectively. Ultraviolet photoelectron spectroscopy (UPS) shows a glimpse of the valence band maximum and Fermi level position. UV, UPS, and photoconductive study simultaneously results in a type II band bending, i.e., staggered type bending is observed at these interfaces. Room temperature weak ferromagnetism along with the insulating nature and a sign of photovoltaic application of these thin films fascinate to carry forward rigorous study from fundamental as well as technological points of view. [ABSTRACT FROM AUTHOR]

Machine-learned interatomic potentials are fast becoming an indispensable tool in computational materials science. One approach is the ephemeral data-derived potential (EDDP), which was designed to accelerate atomistic structure prediction. The EDDP is simple and cost-efficient. It relies on training data generated in small unit cells and is fit using a lightweight neural network, leading to smooth interactions which exhibit the robust transferability essential for structure prediction. Here, we present a variety of applications of EDDPs, enabled by recent developments of the open-source EDDP software. New features include interfaces to phonon and molecular dynamics codes, as well as deployment of the ensemble deviation for estimating the confidence in EDDP predictions. Through case studies ranging from elemental carbon and lead to the binary scandium hydride and the ternary zinc cyanide, we demonstrate that EDDPs can be trained to cover wide ranges of pressures and stoichiometries, and used to evaluate phonons, phase diagrams, superionicity, and thermal expansion. These developments complement continued success in accelerated structure prediction. [ABSTRACT FROM AUTHOR]

Catalyzed by enormous success in the industrial sector, many research programs have been exploring data-driven, machine learning approaches. Performance can be poor when the model is extrapolated to new regions of chemical space, e.g., new bonding types, new many-body interactions. Another important limitation is the spatial locality assumption in model architecture, and this limitation cannot be overcome with larger or more diverse datasets. The outlined challenges are primarily associated with the lack of electronic structure information in surrogate models such as interatomic potentials. Given the fast development of machine learning and computational chemistry methods, we expect some limitations of surrogate models to be addressed in the near future; nevertheless spatial locality assumption will likely remain a limiting factor for their transferability. Here, we suggest focusing on an equally important effort—design of physics-informed models that leverage the domain knowledge and employ machine learning only as a corrective tool. In the context of material science, we will focus on semi-empirical quantum mechanics, using machine learning to predict corrections to the reduced-order Hamiltonian model parameters. The resulting models are broadly applicable, retain the speed of semiempirical chemistry, and frequently achieve accuracy on par with much more expensive ab initio calculations. These early results indicate that future work, in which machine learning and quantum chemistry methods are developed jointly, may provide the best of all worlds for chemistry applications that demand both high accuracy and high numerical efficiency. [ABSTRACT FROM AUTHOR]

Micelles of anionic or zwitterionic surfactants produced depletion interactions between nanofibrils leading to weak gels, while cationic surfactants adsorbed to the nanofibrils, screening repulsions leading to tough gels or phase separation. Are fundamentally and technologically interesting due to their diverse building blocks, network-supporting attractive forces, and gelation pathways. As described below, the 29 articles that appear in this issue shed new light on a wide variety of related topics, including limited valence networks, bridged colloidal networks and coagulant gels, depletant-mediated gels, I in situ i structural characterization of gel aging, mechanical and rheological properties of colloidal networks, hybrid and deformable particle assemblies, and nanocrystal gels with tunable optical response. Bridged networks and coagulant gels Colloidal networks formed by nonspecific bridging polymers represent another interesting class of gel formers, although it can be challenging to design dispersible systems with tunable polymer-colloid interactions. [Extracted from the article]

The enigmatic mechanism underlying unconventional high-temperature superconductivity, especially the role of lattice dynamics, has remained a subject of debate. Theoretical insights have long been hindered due to the lack of an accurate first-principles description of the lattice dynamics of cuprates. Recently, using the r2SCAN meta-generalized gradient approximation (meta-GGA) functional, we have been able to achieve accurate phonon spectra of an insulating cuprate YBa2Cu3O6 and discover significant magnetoelastic coupling in experimentally interesting Cu–O bond stretching optical modes [Ning et al., Phys. Rev. B 107, 045126 (2023)]. We extend this work by comparing Perdew–Burke–Ernzerhof and r2SCAN performances with corrections from the on-site Hubbard U and the D4 van der Waals (vdW) methods, aiming at further understanding on both the materials science side and the density functional side. We demonstrate the importance of vdW and self-interaction corrections for accurate first-principles YBa2Cu3O6 lattice dynamics. Since r2SCAN by itself partially accounts for these effects, the good performance of r2SCAN is now more fully explained. In addition, the performances of the Tao–Mo series of meta-GGAs, which are constructed in a different way from the strongly constrained and appropriately normed (SCAN) meta-GGA and its revised version r2SCAN, are also compared and discussed. [ABSTRACT FROM AUTHOR]

Steelmaking contributes 8% to the total CO2 emissions globally, primarily due to coal-based iron ore reduction. Clean hydrogen-based ironmaking has variable performance because the dominant gas-solid reduction mechanism is set by the defects and pores inside the mm- to nm-sized oxide particles that change significantly as the reaction progresses. While these governing dynamics are essential to establish continuous flow of iron and its ores through reactors, the direct link between agglomeration and chemistry is still contested due to missing measurements. In this work, we directly measure the connection between chemistry and agglomeration in the smallest iron oxides relevant to magnetite ores. Using synthesized spherical 10-nm magnetite particles reacting in H2, we resolve the formation and consumption of wüstite (Fe1-xO)-the step most commonly attributed to whiskering. Using X-ray diffraction, we resolve crystallographic anisotropy in the rate of the initial reaction. Complementary imaging demonstrated how the particles self-assemble, subsequently react, and grow into elongated whisker structures. Our insights into how morphologically uniform iron oxide particles react and agglomerate in H2 reduction enable future size-dependent models to effectively describe the multiscale aspects of iron ore reduction.

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features, such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, DP-range correction, DP long range, graphics processing unit support for customized operators, model compression, non-von Neumann molecular dynamics, and improved usability, including documentation, compiled binary packages, graphical user interfaces, and application programming interfaces. This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, this article presents a comprehensive procedure for conducting molecular dynamics as a representative application, benchmarks the accuracy and efficiency of different models, and discusses ongoing developments. [ABSTRACT FROM AUTHOR]

The MACE architecture represents the state of the art in the field of machine learning force fields for a variety of in-domain, extrapolation, and low-data regime tasks. In this paper, we further evaluate MACE by fitting models for published benchmark datasets. We show that MACE generally outperforms alternatives for a wide range of systems, from amorphous carbon, universal materials modeling, and general small molecule organic chemistry to large molecules and liquid water. We demonstrate the capabilities of the model on tasks ranging from constrained geometry optimization to molecular dynamics simulations and find excellent performance across all tested domains. We show that MACE is very data efficient and can reproduce experimental molecular vibrational spectra when trained on as few as 50 randomly selected reference configurations. We further demonstrate that the strictly local atom-centered model is sufficient for such tasks even in the case of large molecules and weakly interacting molecular assemblies. [ABSTRACT FROM AUTHOR]

In this work, we test a recently developed method to enhance classical auxiliary-field quantum Monte Carlo (AFQMC) calculations with quantum computers against examples from chemistry and material science, representative of classes of industry-relevant systems. As molecular test cases, we calculate the energy curve of H4 and the relative energies of ozone and singlet molecular oxygen with respect to triplet molecular oxygen, which is industrially relevant in organic oxidation reactions. We find that trial wave functions beyond single Slater determinants improve the performance of AFQMC and allow it to generate energies close to chemical accuracy compared to full configuration interaction or experimental results. In the field of material science, we study the electronic structure properties of cuprates through the quasi-1D Fermi–Hubbard model derived from CuBr2, where we find that trial wave functions with both significantly larger fidelities and lower energies over a mean-field solution do not necessarily lead to AFQMC results closer to the exact ground state energy. [ABSTRACT FROM AUTHOR]

Electronic structure calculations have the potential to predict key matter transformations for applications of strategic technological importance, from drug discovery to material science and catalysis. However, a predictive physicochemical characterization of these processes often requires accurate quantum chemical modeling of complex molecular systems with hundreds to thousands of atoms. Due to the computationally demanding nature of electronic structure calculations and the complexity of modern high-performance computing hardware, quantum chemistry software has historically failed to operate at such large molecular scales with accuracy and speed that are useful in practice. In this paper, novel algorithms and software are presented that enable extreme-scale quantum chemistry capabilities with particular emphasis on exascale calculations. This includes the development and application of the multi-Graphics Processing Unit (GPU) library LibCChem 2.0 as part of the General Atomic and Molecular Electronic Structure System package and of the standalone Extreme-scale Electronic Structure System (EXESS), designed from the ground up for scaling on thousands of GPUs to perform high-performance accurate quantum chemistry calculations at unprecedented speed and molecular scales. Among various results, we report that the EXESS implementation enables Hartree–Fock/cc-pVDZ plus RI-MP2/cc-pVDZ/cc-pVDZ-RIFIT calculations on an ionic liquid system with 623 016 electrons and 146 592 atoms in less than 45 min using 27 600 GPUs on the Summit supercomputer with a 94.6% parallel efficiency. [ABSTRACT FROM AUTHOR]

With increasing demand and environmental concerns, researchers are exploring new materials that can perform as well or better than traditional materials while reducing environmental impact. The BAMline, a real-life sample materials research beamline, provides unique insights into materials' electronic and chemical structure at different time and length scales. The beamline specializes in x-ray absorption spectroscopy, x-ray fluorescence spectroscopy, and tomography experiments. This enables real-time optimization of material properties and performance for various applications, such as energy transfer, energy storage, catalysis, and corrosion resistance. This paper gives an overview of the analytical methods and sample environments of the BAMline, which cover non-destructive testing experiments in materials science, chemistry, biology, medicine, and cultural heritage. We also present our own synthesis methods, processes, and equipment developed specifically for the BAMline, and we give examples of synthesized materials and their potential applications. Finally, this article discusses the future perspectives of the BAMline and its potential for further advances in sustainable materials research. [ABSTRACT FROM AUTHOR]

Using quantum computers for computational chemistry and materials science will enable us to tackle problems that are intractable on classical computers. In this paper, we show how the relative energy of defective graphene structures can be calculated by using a quantum annealer. This simple system is used to guide the reader through the steps needed to translate a chemical structure (a set of atoms) and energy model to a representation that can be implemented on quantum annealers (a set of qubits). We discuss in detail how different energy contributions can be included in the model and what their effect is on the final result. The code used to run the simulation on D-Wave quantum annealers is made available as a Jupyter Notebook. This Tutorial was designed to be a quick-start guide for the computational chemists interested in running their first quantum annealing simulations. The methodology outlined in this paper represents the foundation for simulating more complex systems, such as solid solutions and disordered systems. [ABSTRACT FROM AUTHOR]

In this paper, we show that by extending the concept of "microsphere-assisted imaging" into thermal lens (TL) detection, enhancement of photothermal detection is achieved. Furthermore, by integration of TL detection and digital holographic microscopy (DHM), we provide simultaneous measurement of photothermal properties as well as complementary imaging of the sample. Simultaneous acquisition of photothermal properties and imaging is essential for characterization of a variety of samples in biochemistry, material science, and process technologies. TL is a sensitive methodology to detect low concentrations of analytes in low-loss samples, and DHM in the transmission mode is an effective technique for label-free, non-contact, and real-time imaging and measurement of phase objects, such as biomaterials. We demonstrate the usefulness of the TL-DHM integrated system by applying it for acquisition of photothermal maps of samples as well as obtaining their morphometrical information. Additionally, we measure the thermal diffusivity of polydimethylsiloxane because of the focusing effect of polystyrene microspheres. The system has the potential to be applied for a variety of objects including bio-samples and may be proposed as a bench-top characterization device. [ABSTRACT FROM AUTHOR]