Machine learning-guided reconstruction of cytoskeleton network from live-cell AFM images.

How actin filaments (F-actins) are dynamically reorganized in motile cells at the level of individual filaments is an open question. To find the answer, a high-speed atomic force microscopy (HS-AFM) system has been developed to live-imagine intracellular dynamics of the individual F-actins. However, noise and low resolution made it difficult to fully recognize individual F-actins in the HS-AFM images. To tackle this problem, we developed a new machine learning method that quantitatively recognizes individual F-actins. The method estimates F-actin orientation from the image while improving the resolution. We found that F-actins were oriented at ±35° toward the membrane in the lamellipodia, which is consistent with Arp2/3 complex-induced branching. Furthermore, in the cell cortex our results showed non-random orientation at four specific angles, suggesting a new mechanism for F-actin organization demonstrating the potential of our newly developed method to fundamentally improve our understanding of the structural dynamics of F-actin networks.

Regulating oxygen vacancies in ammonium vanadate electrode materials for advanced aqueous zinc ion batteries.

In the past decade, vanadates have attracted one's attention as the electrode materials for aqueous zinc ion batteries (AZIBs). Nevertheless, their structural instability and sluggish ion/electron dynamics lead to an inevitable decline in the electrochemical performance. To address these issues, we introduce oxygen vacancies into NH4V4O10 nanosheets to improve the ion transport rate during the electrochemical reaction. The prepared NHVO-40 samples provide many active sites compared to NH4V4O10 materials. The assembled cell delivers a capacity of 452.03 mAh g-1 at a current density of 0.2 A g-1. It also presents a retention rate of 94.6% at 10 A g-1 after 4000 times cycling. In addition, they still possess an energy density of 332.5 Wh kg-1 at a power density of 70 W kg-1.

Data-driven continuum damage mechanics with built-in physics.

Soft materials such as rubbers and soft tissues often undergo large deformations and experience damage degradation that impairs their function. This energy dissipation mechanism can be described in a thermodynamically consistent framework known as continuum damage mechanics. Recently, data-driven methods have been developed to capture complex material behaviors with unmatched accuracy due to the high flexibility of deep learning architectures. Initial efforts focused on hyperelastic materials, and recent advances now offer the ability to satisfy physics constraints such as polyconvexity of the strain energy density function by default. However, modeling inelastic behavior with deep learning architectures and built-in physics has remained challenging. Here we show that neural ordinary differential equations (NODEs), which we used previously to model arbitrary hyperelastic materials with automatic polyconvexity, can be extended to model energy dissipation in a thermodynamically consistent way by introducing an inelastic potential: a monotonic yield function. We demonstrate the inherent flexibility of our network architecture in terms of different damage models proposed in the literature. Our results suggest that our NODEs re-discover the true damage function from synthetic stress-deformation history data. In addition, they can accurately characterize experimental skin and subcutaneous tissue data.

Physics-Informed Machine Learning for Accurate Prediction of Temperature and Melt Pool Dimension in Metal Additive Manufacturing.

The temperature distribution and melt pool size have a great influence on the microstructure and mechanical behavior of metal additive manufacturing process. The numerical method can give relatively accurate results but is time-consuming and, therefore, unsuitable for in-process prediction. Owing to its remarkable capabilities, machine learning methods have been applied to predict melt pool size and temperature distribution. However, the success of traditional data-driven machine learning methods is highly dependent on the amount and quality of the training data, which is not always convenient to access. This article proposes a physics-informed machine learning (PIML) method, which integrates data and physics laws in the training parts, overcoming the problems of low speed and data availability. An artificial neural network constrained by the heat transfer equation and a small amount of labeled data is developed to predict the melt pool size and temperature distribution. Besides, the locally adaptive activation function is utilized to improve the prediction performance. The result shows that the developed PIML model can accurately predict the temperature and melt pool dimension under different scanning speeds with a small amount of labeled data, which shows significant potential in practical application.

Physics-Informed Machine Learning with Data-Driven Equations for Predicting Organic Solar Cell Performance.

Organic solar cells (OSCs) have emerged as a promising solution in pursuing sustainable energy. This study presents a comprehensive approach to advancing OSC development by integrating data-driven equations from quantum mechanical (QM) descriptors with physics-informed machine learning (PIML) models. We circumvent traditional experimental limitations through high-throughput QM calculations, prioritizing transparent and interpretable models. Using the SISSO++ method, we identified key descriptors that effectively map the relationships between input variables and photovoltaic performance metrics. Our innovative predictive models, derived from SISSO outputs, excel in forecasting critical OSC parameters such as short-circuit current (JSC), open-circuit voltage (VOC), fill factor (FF), and power conversion efficiency (PCEmax), achieving high accuracy even with limited data sets. To validate our models' practical utility, we applied the PIML framework to a newly compiled data set of OSC devices, demonstrating their versatility and capability in pinpointing high-performance materials. This research underscores the strong predictive power of our models, bridging the gap between experimental results and theoretical predictions and making significant contributions to the advancement of sustainable energy technologies.

High-resolution lensless holographic microscopy using a physics-aware deep network.

Significance: Lensless digital inline holographic microscopy (LDIHM) is an emerging quantitative phase imaging modality that uses advanced computational methods for phase retrieval from the interference pattern. The existing end-to-end deep networks require a large training dataset with sufficient diversity to achieve high-fidelity hologram reconstruction. To mitigate this data requirement problem, physics-aware deep networks integrate the physics of holography in the loss function to reconstruct complex objects without needing prior training. However, the data fidelity term measures the data consistency with a single low-resolution hologram without any external regularization, which results in a low performance on complex biological data. Aim: We aim to mitigate the challenges with trained and physics-aware untrained deep networks separately and combine the benefits of both methods for high-resolution phase recovery from a single low-resolution hologram in LDIHM. Approach: We propose a hybrid deep framework (HDPhysNet) using a plug-and-play method that blends the benefits of trained and untrained deep models for phase recovery in LDIHM. The high-resolution phase is generated by a pre-trained high-definition generative adversarial network (HDGAN) from a single low-resolution hologram. The generated phase is then plugged into the loss function of a physics-aware untrained deep network to regulate the complex object reconstruction process. Results: Simulation results show that the SSIM of the proposed method is increased by 0.07 over the trained and 0.04 over the untrained deep networks. The average phase-SNR is elevated by 8.2 dB over trained deep models and 9.8 dB over untrained deep networks on the experimental biological cells (cervical cells and red blood cells). Conclusions: We showed improved performance of the HDPhysNet against the unknown perturbation in the imaging parameters such as the propagation distance, the wavelength of the illuminating source, and the imaging sample compared with the trained network (HDGAN). LDIHM, combined with HDPhysNet, is a portable and technology-driven microscopy best suited for point-of-care cytology applications.

Microscopic and macroscopic analysis of hexavalent chromium adsorption on polypyrrole-polyaniline@rice husk ash adsorbent using statistical physics modeling.

This paper contributed with new findings to understand and characterize a heavy metal adsorption on a composite adsorbent. The synthesized polypyrrole-polyaniline@rice husk ash (PPY-PANI@RHA) was prepared and used as an adsorbent for the removal of hexavalent chromium Cr(VI). The adsorption isotherms of Cr(VI) ions on PPY-PANI@RHA were experimentally determined at pH 2, and at different adsorption temperatures (293, 303, and 313 K). Multi-layer model developed using statistical physics formalism was applied to theoretically analyze and characterize the different interactions and ion exchanges during the adsorption process for the elimination of this toxic metal from aqueous solutions, and to attribute new physicochemical interpretation of the process of adsorption. The physicochemical structures and properties of the synthesized PPY-PANI@RHA were characterized via Fourier transform infrared spectroscopy (FTIR). Fitting findings showed that the mechanism of adsorption of Cr(VI) on PPY-PANI@RHA was a multi-ionic mechanism, where one binding site may be occupied by one and two ions. It may also be noticed that the temperature augmentation generated the activation of more functional groups of the composite adsorbent, facilitating the interactions of metal ions with the binding sites and the access to smaller pore. The energetic characterization suggested that the mechanism of adsorption of the investigated systems was exothermic and Cr(VI) ions were physisorbed on PPY-PANI@RHA surface via electrostatic interaction, reduction of Cr(VI) to Cr(III), hydrogen bonding, and ion exchange. Overall, the utilization of the theory of statistical physics provided fruitful and profounder analysis of the adsorption mechanism. The estimation of the pore size distribution (PSD) of the polypyrrole-polyaniline@rice husk ash using the statistical physics approach was considered stereographic characterization of the adsorbent (here PPY-PANI@RHA was globally a meso-porous adsorbent). Lastly, the mechanism of Cr(VI) removal from wastewater using PPY-PANI@RHA as adsorbent was macroscopically investigated via the estimation of three thermodynamic functions.

Mechanism of local electric oxidation on two-dimensional MoS2 for resistive memory application.

The manipulation and mechanism of two-dimensional (2D) transition metal dichalcogenides (TMDs) by external electric field are significant to the photoelectric properties. Herein, the 2D MoS2 nanosheets were oxidized to form MoS2-MoO3 local heterojunctions by an electric field, applied in multistable memristors for the proposal of NanoQR code. A modified thermal oxidation model was derived to reveal the mechanism of local electric oxidation on 2D MoS2. From current-voltage curves, the barrier height of the MoS2 device showed an increase of 0.39 eV due to local oxidation after applying voltage for 480 s. Based on density-functional theory, the increase of barrier height was calculated as 0.38 eV between MoS2-MoS2 and MoS2-MoO3 supercells. The 2D MoS2-MoO3 local heterojunctions were further applied as multistable memory storage at the nanoscale. The findings suggest a novel strategy for controlling local electric oxidation on 2D TMDs to manipulate the properties for the application of photoelectric memory nanodevices.

A comprehensive study of various superconductors for superconducting nanowire single photon detectors applications.

Research activities in the field of superconducting nanowire single photon detectors (SNSPDs) have exhibited major progress over the last two decades. The low dark count rate, low jitter time, low recovery time, and ultrafast response time in an extended wavelength range, along with several improvements in the material parameters, cryogenic environment, and associated electronics make SNSPDs a superior choice over other photo-detectors. The struggle in simultaneously optimizing these parameters made the pace of SNSPD research steady, until the report of unit system detection efficiency at low temperatures for WSi SNSPD. Due to the difficulty in maintaining the low temperature for a long time, researchers are currently focusing on using high transition temperatures cuprate-based superconductors. These have the added advantages of making a portable SNSPD combined with faster response dynamics required for commercial SNSPD applications. In this review, we have discussed different models for single photon detection, followed by research activities carried out employing different superconducting materials over the last 20 years. The ongoing research toward utilizing oxide-based superconductors as photon detection devices along with a few suggestions for improving the device performance is discussed. This review will fill the gap required for a detailed study of different classes of superconductors for SNSPD applications.

Physics-Informed Graph Neural Networks to solve 1-D equations of blood flow.

BACKGROUND AND OBJECTIVE: Computational models of hemodynamics can contribute to optimizing surgical plans, and improve our understanding of cardiovascular diseases. Recently, machine learning methods have become essential to reduce the computational cost of these models. In this study, we propose a method that integrates 1-D blood flow equations with Physics-Informed Graph Neural Networks (PIGNNs) to estimate the propagation of blood flow velocity and lumen area pulse waves along arteries. METHODS: Our methodology involves the creation of a graph based on arterial topology, where each 1-D line represents edges and nodes in the blood flow analysis. The innovation lies in decoding the mathematical data connecting the nodes, where each node has velocity and lumen area pulse waveform outputs. The training protocol for PIGNNs involves measurement data, specifically velocity waves measured from inlet and outlet vessels and diastolic lumen area measurements from each vessel. To optimize the learning process, our approach incorporates fundamental physical principles directly into the loss function. This comprehensive training strategy not only harnesses the power of machine learning but also ensures that PIGNNs respect fundamental laws governing fluid dynamics. RESULTS: The accuracy was validated in silico with different arterial networks, where PIGNNs achieved a coefficient of determination (R2) consistently above 0.99, comparable to numerical methods like the discontinuous Galerkin scheme. Moreover, with in vivo data, the prediction reached R2 values greater than 0.80, demonstrating the method's effectiveness in predicting flow and lumen dynamics using minimal data. CONCLUSIONS: This study showcased the ability to calculate lumen area and blood flow rate in blood vessels within a given topology by seamlessly integrating 1-D blood flow with PIGNNs, using only blood flow velocity measurements. Moreover, this study is the first to compare the PIGNNs method with other classic Physics-Informed Neural Network (PINNs) approaches for blood flow simulation. Our findings highlight the potential to use this cost-effective and proficient tool to estimate real-time arterial pulse waves.

Cosmology and fundamental physics with the ELT-ANDES spectrograph.

State-of-the-art 19th century spectroscopy led to the discovery of quantum mechanics, and 20th century spectroscopy led to the confirmation of quantum electrodynamics. State-of-the-art 21st century astrophysical spectrographs, especially ANDES at ESO's ELT, have another opportunity to play a key role in the search for, and characterization of, the new physics which is known to be out there, waiting to be discovered. We rely on detailed simulations and forecast techniques to discuss four important examples of this point: big bang nucleosynthesis, the evolution of the cosmic microwave background temperature, tests of the universality of physical laws, and a real-time model-independent mapping of the expansion history of the universe (also known as the redshift drift). The last two are among the flagship science drivers for the ELT. We also highlight what is required for the ESO community to be able to play a meaningful role in 2030s fundamental cosmology and show that, even if ANDES only provides null results, such 'minimum guaranteed science' will be in the form of constraints on key cosmological paradigms: these are independent from, and can be competitive with, those obtained from traditional cosmological probes.

Theory and Measurement of Heat Transport in Solids: How Rigidity and Spectral Properties Govern Behavior.

Models of heat transport in solids, being based on idealized elastic collisions of gas molecules, are flawed because heat and mass diffuse independently in solids but together in gas. To better understand heat transfer, an analytical, theoretical approach is combined with data from laser flash analysis, which is the most accurate method available. Dimensional analysis of Fourier's heat equation shows that thermal diffusivity (D) depends on length-scale, which has been confirmed experimentally for metallic, semiconducting, and electrically insulating solids. A radiative diffusion model reproduces measured thermal conductivity (K = DρcP = D × density × specific heat) for thick solids from ~0 to >1200 K using idealized spectra represented by 2-4 parameters. Heat diffusion at laboratory temperatures (conduction) proceeds by absorption and re-emission of infrared light, which explains why heat flows into, through, and out of a material. Because heat added to matter performs work, thermal expansivity is proportional to ρcP/Young's modulus (i.e., rigidity or strength), which is confirmed experimentally over wide temperature ranges. Greater uptake of applied heat (e.g., cP generally increasing with T or at certain phase transitions) reduces the amount of heat that can flow through the solid, but because K = DρcP, the rate (D) must decrease to compensate. Laser flash analysis data confirm this proposal. Transport properties thus depend on heat uptake, which is controlled by the interaction of light with the material under the conditions of interest. This new finding supports a radiative diffusion mechanism for heat transport and explains behavior from ~0 K to above melting.

CT perfusion parameter estimation in stroke using neural network with transformer and physical model priors.

OBJECTIVES: CT perfusion (CTP) imaging is vital in treating acute ischemic stroke by identifying salvageable tissue and the infarcted core. CTP images allow quantitative estimation of CT perfusion parameters, which can provide information on the degree of tissue hypoperfusion and its salvage potential. Traditional methods for estimating perfusion parameters, such as singular value decomposition (SVD) and its variations, are known to be sensitive to noise and inaccuracies in the arterial input function. To our knowledge, there has been no implementation of deep learning methods for CT perfusion parameter estimation. MATERIALS & METHODS: In this work, we propose a deep learning method based on the Transformer model, named CTPerformer-Net, for CT perfusion parameter estimation. In addition, our method incorporates some physical priors. We integrate physical consistency prior, smoothness prior and the physical model prior through the design of the loss function. We also generate a simulation dataset based on physical model prior for training the network model. RESULTS: In the simulation dataset, CTPerformer-Net exhibits a 23.4 % increase in correlation coefficients, a 95.2 % decrease in system error, and a 90.7 % reduction in random error when contrasted with block-circulant SVD. CTPerformer-Net successfully identifies hypoperfused and infarcted lesions in 103 real CTP images from the ISLES 2018 challenge dataset. It achieves a mean dice score of 0.36 for the infarct core segmentation, which is slightly higher than the commercially available software (dice coefficient: 0.34) used as a reference level by the challenge. CONCLUSION: Experimental results on the simulation dataset demonstrate that CTPerformer-Net achieves better performance compared to block-circulant SVD. The real-world patient dataset confirms the validity of CTPerformer-Net.