TDTR实验、热物性调控、机器学习

ABSTRACT To advance thermal control technology, improve thermal reuse efficiency, and further enhance device performance, it is crucial to understand microscopic spatial and temporal heat transport in materials. In this study, we developed a high-throughput time-domain thermoreflectance (HT-TDTR) technique that accelerates the measurement speed of thermophysical properties. The fundamental concept involves decomposing supercontinuum light into a pump pulse (1064 nm) and multiple delayed probe pulses (900 nm–730 nm) with different delays, enabling simultaneous acquisition of thermoreflectance signals at multiple delay times. Quartz glass, SrTiO3 (100) single crystal, and c-plane sapphire were heated with picosecond pulsed light, and the temporal temperature decrease at six delay times was simultaneously measured. The thermal effusivities analyzed based on heat diffusion equation were consistent with the literature values. Furthermore, we applied machine learning-based analysis and demonstrated the ability to determine thermophysical properties from measurement data consisting of only a few delay points. With sufficient signal strength, machine learning can predict a reasonable thermal effusivity based on experimental data obtained in less than a second. HT-TDTR enables rapid and accurate measurement of samples based on information about thermal relaxation dynamics, facilitating more efficient characterization of thermophysical properties. GRAPHICAL ABSTRACT IMPACT STATEMENT Our high-throughput TDTR technique instantly captures picosecond-to-nanosecond thermal dynamics in materials, advancing the understanding of spatiotemporal heat transport and facilitating the development of next-generation thermal management materials.

Lithium niobate (LiNbO3, LN) has unique electro‐optic and piezoelectric properties, making it widely used in optical devices, telecommunications, sensors, and acoustic systems. Thermal conductivity κ is a critical property influencing the performance and reliability of these applications. Point defects commonly exist in LN and can significantly reduce its κ. However, the effects of point defects on thermal transport in LN remain poorly understood. In this work, LN crystals are prepared through thermal reduction at 600–800 °C, inducing a depth‐dependent distribution of oxygen vacancies (VO) that increases in concentration with increasing reduction temperature. Time‐domain thermoreflectance and square‐pulsed source measurements reveal a significant suppression and a notable gradient in κ, attributed to the depth‐dependent distribution of VO. A machine learning potential with ab initio accuracy is developed to simulate the impact of typical point defects on thermal transport in LN, demonstrating that VO predominantly suppresses κ by affecting the transport of low‐frequency phonons below 6 THz. Notably, niobium vacancies and antisite defects exhibit similar effects, whereas lithium vacancies show minimal impact. This work highlights the dominant role of VO in modulating κ and provides insights into defect engineering for advanced LN‐based devices and similar ferroelectric crystals.

Accurate prediction of the thermal conductivity (κ) of polymers is generally challenging due to their complex structures. Currently available ab initio methods (e.g., DFT-BTE) are prohibitively expensive, and the classical force fields used in molecular dynamics lack accuracy. In this study, we combine ab initio hybrid machine learning (ML)/multipolar polarizable potential (i.e., PhyNEO) with ML-facilitated heat flux calculation. This approach provides reliable heat flux trajectories, which are then used to predict polymer κ quantitatively. Using poly(ethylene oxide) as an example, we compare our calculation results with reliable experimental reference obtained from time-domain thermoreflectance measurement, reaching excellent agreement. This work enables the quantitative prediction of bulk polymer κ starting from only small cluster quantum data, warranting broad applications in future.

Characterizing spatially varying thermal conductivities is significant to unveil the structure–property relation for a wide range of thermal functional materials such as chemical-vapor-deposited (CVD) diamonds, ion-irradiated materials, nuclear materials under radiation, and battery electrode materials. Although the development of thermal property microscopy based on time/frequency-domain thermoreflectance (TDTR/FDTR) enabled in-plane scanning of a thermal conductivity profile, measuring depth-dependent profiles remains challenging. This work proposed a machine-learning-based reconstruction method for extracting depth-dependent thermal conductivity [Formula: see text] directly from pump–probe phase signals. We demonstrated that the simple supervised-learning algorithm kernel ridge regression can reconstruct [Formula: see text] without requiring pre-knowledge about the functional form of the profile. The reconstruction method can not only accurately reproduce typical [Formula: see text] distributions such as the exponential profile of CVD diamonds and the Gaussian profile of ion-irradiated materials but also complex profiles artificially constructed by superimposing Gaussian, exponential, polynomial, and logarithmic functions. In addition to FDTR, the method also shows excellent performance of reconstructing [Formula: see text] of ion-irradiated semiconductors from TDTR signals. This work demonstrates that combining machine learning with pump–probe thermoreflectance is an effective way for depth-dependent thermal property mapping.

Lattice thermal conductivity is a critical parameter for assessing the thermal transport properties of materials. When confined to the monolayer limit, two‐dimensional materials display unique thermal characteristics distinct from their three‐dimensional counterparts. This article first provides a concise summary of three widely applied experimental methodologies—Raman thermometry, suspended microbridge techniques, and time‐domain thermoreflectance—and their utility in validating theoretical predictions. It subsequently delves into recent advancements in theoretical modeling, encompassing both equilibrium and nonequilibrium molecular dynamics studies; first‐principles calculations grounded in the phonon Boltzmann transport equation that account for higher‐order scattering phenomena such as four‐phonon processes and phonon–electron interactions; emerging methods based on normal mode analysis for detailed phonon contribution decomposition; and novel approaches employing the Wigner transport equation to unify the description of phonon coherence and wave‐like heat transport phenomena beyond conventional theoretical frameworks. In addition, the advent of machine learning has expanded the scope of direct thermal conductivity prediction and the development of high‐precision interatomic potentials, paving the way for high‐throughput screening and extensive simulations. This review contrasts the advantages and drawbacks of these methodologies, identifies key challenges facing the field, and sketches future directions for 2D thermal transport research, emphasizing the integration of multiscale modeling, data‐driven innovation, and the synergy between experiments and theoretical insights.

No abstract available

Moisture has a detrimental impact on both human health and the durability of buildings, as it weakens construction materials and reduces their mechanical properties, including compressive strength. Therefore, monitoring the moisture content of materials is essential for maintaining the quality and safety of structures. One method that enables the indirect determination of moisture levels is time-domain reflectometry (TDR). In this study, machine learning models, including Gaussian Process Regression (GPR), Decision Tree, Linear Regression, and Linear SVM, were employed to improve the accuracy of moisture prediction in autoclaved aerated concrete (AAC) with a density of 700 kg/m3. The results demonstrate that the application of machine learning algorithms provides higher precision in material assessment compared to traditional analytical methods.

This study employed terahertz time-domain spectroscopy (THz-TDS) to acquire spectral signals of wood samples with different densities and extract their refractive indices. Wood density prediction models were developed using three machine learning algorithms: Elastic Net Regression (ENR), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost). The Uninformative Variable Elimination (UVE) algorithm was used for the feature selection of refractive index spectra, and the Particle Swarm Optimization (PSO) algorithm was applied to optimize model hyperparameters. Meanwhile, the Shapley Additive exPlanations (SHAP) method was employed to perform interpretive analysis on the optimal model. The results showed a positive correlation between wood density and THz refractive index. In the task of predicting the wood density of fast-growing tree species, the XGBoost model demonstrated excellent performance, with a test set coefficient of determination (R2) of 0.9594. When the dataset was expanded to include fast-growing wood species and high-density Pterocarpus wood species, the test set R2 increased to 0.9846, fully validating the universality and effectiveness of the method in cross-species wood density prediction. After feature selection using the UVE algorithm, the test set R2 was further improved to 0.9862, while significantly reducing computational complexity. SHAP feature importance analysis revealed that the refractive index at 0.216 THz made the greatest contribution to the wood density prediction model, and the top 9 key feature frequencies were all located in the 0.2-0.3 THz low-frequency band, all exhibiting significant positive impacts on the prediction model. This study demonstrates that the integrated application of terahertz time-domain spectroscopy technology and machine learning algorithms provides an innovative solution for rapid and precise detection of wood density.

—In the sustainable packaging industry, multiple parameters require regulation to achieve a high-quality final product that meets contemporary demands. In bioplastic manufacturing, the control of the film thickness is critical because it influences the mechanical properties and other key characteristics. Terahertz time-domain spectroscopy (THz-TDS) has emerged as a promising technology for the non-invasive characterization of polymeric materials. The present study evaluates the integration of THz-TDS with chemometric techniques and machine learning models to predict the thickness of bioplastic samples fabricated from potato and maize starch. Three distinct thickness levels were produced by solution casting, and a spectral analysis was performed in the range of 0.5 to 1.2 THz. Four regression models were developed, including partial least squares regression, support vector regression, binary regression tree, and a feedforward neural network. The performance of the model was assessed using the coefficient of determination (R 2 ), root mean square error (RMSE) and the ratio of performance to deviation (RPD). R 2 values ranged from 0.8379 to 0.9757, the RMSE values ranged from 0.1259 to 0.3368, and the RPD values ranged from 2.4399 to 6.8106. These findings underscore the potential of THz-TDS and machine learning for non-invasive analysis of thin polymeric films and lay the groundwork for future research aimed at enhancing reliability and functionality.

The moisture content of building materials, especially in porous media, such as cellular concrete, is a serious problem affecting the durability, quality of thermal insulation and safety of structures. Traditional methods of moisture assessment, based on gravimetric laboratory analyses, are time-consuming. The alternatives are the indirect techniques, such as time-domain reflectometry (TDR), which allow for quick measurements by analyzing the dielectric properties of materials. The aim of this work was to develop predictive models estimating moisture content of cellular concrete depending on its apparent density and other material parameters. The article used both classical regression models θ(ε ), θ(ε,ρ) and artificial intelligence methods, including neural networks (NN), regression trees, support vector machines (SVM) and gaussian process regression (GPR) models. The θ(ε) model performed best at cellular concrete type 400 kg/m³ (R² = 0.9433), but its accuracy declined at higher densities. The universal θ(ε,ρ) model gives better results at 500 and 600 kg/m³ type cellular concretes, with an overall R² of 0.9340. However, AI models outperformed both of them. The GPR model achieved near-perfect predictions (R² ≈ 0.9999, RMSE = 0.0021 – 0.0032 cm³/cm³), while SVM and NN also showed high accuracy (R² = 0.9914–0.9960) with significantly lower errors than deterministic regression models. Comparison of the effectiveness of these approaches allowed for the assessment of the accuracy of moisture prediction based on different data sources. The obtained results in - dicate the significant potential of AI application in monitoring the moisture content of building materials, offering more effective and precise diagnostic tools compared to traditional methods.

This article shows the possibility to adopt Support Vector Machine (SVM) learning method to predict moisture of building materials measured by the Time Domain Reflectometry (TDR) method. TDR is an indirect technique of moisture detection. It enables to evaluate apparent permittivity of moist material and then predict moisture using physical or empirical models. In this research it is presented the method that avoids evaluation of apparent permittivity value and estimate moisture basing on the raw TDR waveforms. SVM is one of the most popular machine learning methods that could be used both for classification and regression modelling. It is mostly applied for analysing of multidimensional signals, but could be also applied to evaluate moisture from raw TDR signals. SVM regression model allows quick estimation of material moisture and achieve similar or better measurement accuracy comparing to the standard calibration methods. Research was conducted on two types of building materials – the red and the silicate bricks and data analysis confirmed the suitability of SVM models in determining moisture content using the TDR method.

Abstract Terahertz waves hold significant potential for applications in wood identification, owing to their good penetration and distinctive fingerprints in wood. This study focuses on wood samples from five different Guibourtia species as the research objects. The terahertz time-domain spectroscopy (THz-TDS) is employed to acquire the spectroscopic signals of the wood samples and to extract their optical parameter data. The THz refractive indices are dimensionally reduced through principal component analysis (PCA), and three machine learning models, namely partial least squares-discriminant analysis (PLS-DA), random forest (RF), and support vector machine (SVM), are employed to classify the wood of five different Guibourtia species. Time delays of the wood samples from five different Guibourtia species are concentrated in the range of 60–62 ps and exhibit different amplitudes in the frequency domain. Refractive indices showed significant variations within the THz band. PCA for dimensionality reduction of terahertz time-domain spectral data significantly improves the recognition rate of machine learning models. Applying PCA to the refractive index data, the RF model achieves a highest recognition rate of 96.9 % and an overall classification accuracy of 98 %. Current results demonstrate that THz-TDS enables rapid, accurate, and non-destructive classification and identification of wood from the Guibourtia species.

Several optical spectroscopy and imaging techniques have already proven their ability to identify different plastic types found in household waste. However, most common optical techniques feasible for plastic sorting, struggle to measure black plastic objects due to the high absorption at visible and near-infrared wavelengths. In this study, 12 black samples of nine different materials have been characterized with Fourier-transform infrared spectroscopy (FTIR), hyperspectral imaging, and terahertz time-domain spectroscopy (THz-TDS). While FTIR validated the plastic types of the samples, the hyperspectral camera using visible and near-infrared wavelengths was challenged to measure the samples. The THz-TDS technique was successfully able to measure the samples without direct sample contact under ambient conditions. From the recorded terahertz waveforms the refractive index and absorption coefficient are extracted for all samples in the range from 0.4 to 1.0 THz. Subsequently, the obtained values were projected onto a two-dimensional map to discriminate the materials using the classifiers k-Nearest Neighbours, Bayes, and Support Vector Machines. A classification accuracy equal to unity was obtained, which proves the ability of THz-TDS to discriminate common black plastics.

Antiferroelectric (AFE) materials have received great attention because of their potential applications in the energy sector. Nevertheless, the properties of AFE materials have not been explored for a long time, especially the atomic‐scale understanding of AFE domain walls. Here, using first‐principles‐based machine learning potentials, we identify the atomic structures, energies, and dynamic properties of the domain walls for AFE lead zirconate. It is found that the domain wall can reduce the critical antiferroelectric‐ferroelectric transition field. During the electric field‐driven polarization switching process, the domain wall is immobile. Importantly, we observe that a distinct domain structure spontaneously forms in bulk lead zirconate upon annealing at 300 K. The domain structure exhibits an alternating array of clockwise–anticlockwise vortexes along radial with continuous polarization rotation. This anomalous AFE vortex is derived from the energy degeneracy in four possible orientations of the polarization order, which can enhance the dielectric response in the terahertz. The current results give an implication for the emergence of AFE vortex in AFE materials as well as ferroelectric materials.

Accurate estimation of battery state of health is crucial for effective electric vehicle battery management. Here, we propose five health indicators that can be extracted online from real-world electric vehicle operation and develop a machine learning-based method to estimate the battery state of health. The proposed indicators provide physical insights into the energy and power fade of the battery and enable accurate capacity estimation even with partially missing data. Moreover, they can be computed for portions of the charging profile and real-world driving discharging conditions, facilitating real-time battery degradation estimation. The indicators are computed using experimental data from five cells aged under electric vehicle conditions, and a linear regression model is used to estimate the state of health. The results show that models trained with power autocorrelation and energy-based features achieve capacity estimation with maximum absolute percentage error within 1.5% to 2.5%. Andrea Lanubile and colleagues develop a machine learning-based algorithm to estimate battery state of health during real world operations. The proposed method leads to highly accurate estimation even when partial battery data are missing.