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Generalisation across multiple tasks is a major challenge in deep learning for medical imaging applications, as it can cause a catastrophic forgetting problem. One commonly adopted approach to address these challenges is to train the model from scratch, incorporating old and new data, classes, and tasks. However, this solution comes with its downsides, as it is time-consuming, requires high computational resources, is susceptible to bias, and lacks flexibility. To effectively address these issues, this paper introduces a generalisable DL framework that consists of three key components: self-supervised learning, feature fusion of a single task, and feature fusion of new classes or tasks. Using the proposed framework, DL models with the SVM classifier can accurately detect abnormalities in X-ray tasks, including the humerus and wrist, achieving an accuracy of 92.71% and 90.74%, respectively. These results were achieved using a single classifier with minimal training requirements when new tasks were introduced. Another experiment was performed on chest X-rays, where new classes were added to the pre-existing ones. Without requiring retraining with both old and new classes, our framework achieved a combined class accuracy of 98.18%. This demonstrates that the model has not forgotten the old data. The proposed framework enhances performance and brings flexibility and efficiency to the training process, saving time and computational resources.

Adversarial attacks pose a significant threat to deep learning models, specifically medical images, as they can mislead models into making inaccurate predictions by introducing subtle distortions to the input data that are often imperceptible to humans. Although adversarial training is a common technique used to mitigate these attacks on medical images, it lacks the flexibility to address new attack methods and effectively improve feature representation. This paper introduces a novel Model Ensemble Feature Fusion (MEFF) designed to combat adversarial attacks in medical image applications. The proposed model employs feature fusion by combining features extracted from different DL models and then trains Machine Learning classifiers using the fused features. It uses a concatenation method to merge the extracted features, forming a more comprehensive representation and enhancing the model's ability to classify classes accurately. Our experimental study has performed a comprehensive evaluation of MEFF, considering several challenging scenarios, including 2D and 3D images, greyscale and colour images, binary classification, and multi-label classification. The reported results demonstrate the robustness of using MEFF against different types of adversarial attacks across six distinct medical image applications. A key advantage of MEFF is its capability to incorporate a wide range of adversarial attacks without the need to train from scratch. Therefore, it contributes to developing a more diverse and robust defence strategy. More importantly, by leveraging feature fusion and ensemble modelling, MEFF enhances the resilience of DL models in the face of adversarial attacks, paving the way for improved robustness and reliability in medical image analysis.

The development of dimensionally stable anodes (DSAs) has made the chlorine evolution reaction (CER) the most important industrial anode reaction since the 1960s. However, the preparation of DSA depends on the extensive use of precious metals, Ru and Ir, which are expensive and scarce. Herein, a cationic defect adsorption–oxidation anchoring strategy to immobilize oxidized sub‐nano ruthenium clusters on 2D low‐crystallinity titanium oxide (2D TiOx) substrate is reported. Through the metal oxide−support interaction, the 2D TiOx alters the electronic structure of ruthenium oxide (RuOx), improving its activity, selectivity, and stability for CER. Specifically, the mass activity of the RuOx/2D TiOx electrode is 26.5 and 143.5 times higher than that of the state‐of‐the‐art commercial RuO2 and DSA, respectively, at an overpotential of 100 mV. Moreover, the selectivity of the RuOx/2D TiOx electrode to CER is approximately 96.5%, and it exhibits remarkable durability lasting for over 210 h. Therefore, the 2D TiOx substrate holds significant potential for improving the dispersion, active site density, and atomic utilization of oxidized sub‐nano noble metal clusters.

Musculoskeletal conditions affect an estimated 1.7 billion people worldwide, causing intense pain and disability. These conditions lead to 30 million emergency room visits yearly, and the numbers are only increasing. However, diagnosing musculoskeletal issues can be challenging, especially in emergencies where quick decisions are necessary. Deep learning (DL) has shown promise in various medical applications. However, previous methods had poor performance and a lack of transparency in detecting shoulder abnormalities on X-ray images due to a lack of training data and better representation of features. This often resulted in overfitting, poor generalisation, and potential bias in decision-making. To address these issues, a new trustworthy DL framework has been proposed to detect shoulder abnormalities (such as fractures, deformities, and arthritis) using X-ray images. The framework consists of two parts: same-domain transfer learning (TL) to mitigate imageNet mismatch and feature fusion to reduce error rates and improve trust in the final result. Same-domain TL involves training pre-trained models on a large number of labelled X-ray images from various body parts and fine-tuning them on the target dataset of shoulder X-ray images. Feature fusion combines the extracted features with seven DL models to train several ML classifiers. The proposed framework achieved an excellent accuracy rate of 99.2%, F1Score of 99.2%, and Cohen's kappa of 98.5%. Furthermore, the accuracy of the results was validated using three visualisation tools, including gradient-based class activation heat map (Grad CAM), activation visualisation, and locally interpretable model-independent explanations (LIME). The proposed framework outperformed previous DL methods and three orthopaedic surgeons invited to classify the test set, who obtained an average accuracy of 79.1%. The proposed framework has proven effective and robust, improving generalisation and increasing trust in the final results.

Abstract Data scarcity is a major challenge when training deep learning (DL) models. DL demands a large amount of data to achieve exceptional performance. Unfortunately, many applications have small or inadequate data to train DL frameworks. Usually, manual labeling is needed to provide labeled data, which typically involves human annotators with a vast background of knowledge. This annotation process is costly, time-consuming, and error-prone. Usually, every DL framework is fed by a significant amount of labeled data to automatically learn representations. Ultimately, a larger amount of data would generate a better DL model and its performance is also application dependent. This issue is the main barrier for many applications dismissing the use of DL. Having sufficient data is the first step toward any successful and trustworthy DL application. This paper presents a holistic survey on state-of-the-art techniques to deal with training DL models to overcome three challenges including small, imbalanced datasets, and lack of generalization. This survey starts by listing the learning techniques. Next, the types of DL architectures are introduced. After that, state-of-the-art solutions to address the issue of lack of training data are listed, such as Transfer Learning (TL), Self-Supervised Learning (SSL), Generative Adversarial Networks (GANs), Model Architecture (MA), Physics-Informed Neural Network (PINN), and Deep Synthetic Minority Oversampling Technique (DeepSMOTE). Then, these solutions were followed by some related tips about data acquisition needed prior to training purposes, as well as recommendations for ensuring the trustworthiness of the training dataset. The survey ends with a list of applications that suffer from data scarcity, several alternatives are proposed in order to generate more data in each application including Electromagnetic Imaging (EMI), Civil Structural Health Monitoring, Medical imaging, Meteorology, Wireless Communications, Fluid Mechanics, Microelectromechanical system, and Cybersecurity. To the best of the authors’ knowledge, this is the first review that offers a comprehensive overview on strategies to tackle data scarcity in DL.

Abstract Abstract Gold nanoparticles (AuNPs) are promising materials for many bioapplications. However, upon contacting with biological media, AuNPs undergo changes. The interaction with proteins results in the so‐called protein corona (PC) around AuNPs, leading to the new bioidentity and optical properties. Understanding the mechanisms of PC formation and its functions can help us to utilise its benefits and avoid its drawbacks. To date, most of the previous works aimed to understand the mechanisms governing PC formation and focused on the spherical nanoparticles, although non‐spherical nanoparticles are designed for a wide range of applications in biosensing. In this work, we investigated the differences in PC formation on spherical and anisotropic AuNPs (nanostars in particular) from the joint experimental (extinction spectroscopy, zeta potential and surface‐enhanced Raman scattering [SERS]) and computational methods (the finite element method and molecular dynamics [MD] simulations). We discovered that protein does not fully cover the surface of anisotropic nanoparticles, leaving SERS hot‐spots at the tips and high curvature edges ‘available’ for analyte binding (no SERS signal after pre‐incubation with protein) while providing protein‐induced stabilization (indicated by extinction spectroscopy) of the AuNPs by providing a protein layer around the particle's core. The findings are confirmed from our MD simulations, the adsorption energy significantly decreases with the increased radius of curvature, so that tips (adsorption energy: 2762.334 kJ/mol) would be the least preferential binding site compared to core (adsorption energy: 11819.263 kJ/mol). These observations will help the development of new nanostructures with improved sensing and targeting ability.

Low dimensional sp3 carbon nanostructures have attracted increasing attention recently, due to their unique properties and appealing applications. Based on in silico studies, this work exploits the impacts from functional groups on the tensile properties of carbon nanothreads (NTH) – a new sp3 carbon nanostructure. It is found that functional groups will alter the local bond configuration and induce initial stress concentration, which significantly reduces the fracture strain/strength of NTH. Different functional types lead to different local bond re-configurations, and introduce different impacts on NTH. Further studies reveal that the tensile properties decreases generally when the content of functional groups increases. However, some NTHs with higher content of functional groups exhibit higher fracture strain/strength than their counterparts with lower percentage. Such observations are attributed to the synergetic effects from the sample length, self-oscillation, and distribution of functional groups. Simulations show that the tensile behaviour of NTH with the same functional percentage differs when the distribution pattern varies. Overall, ethyl groups are found to induce larger degradation on the tensile properties of NTH than methyl and phenyl groups. This study provides a comprehensive understanding of the influence from functional groups, which should be beneficial to the engineering applications of NTH.

Abstract Metal hydrides (MH) are known as one of the most suitable material groups for hydrogen energy storage because of their large hydrogen storage capacity, low operating pressure, and high safety. However, their slow hydrogen absorption kinetics significantly decreases storage performance. Faster heat removal from MH storage can play an essential role to enhance its hydrogen absorption rate, resulting in better storage performance. In this regard, the present study aims to improve heat transfer performance to positively impact the hydrogen absorption rate of MH storage systems. A novel semi-cylindrical coil is first designed and optimized for hydrogen storage and embedded as an internal heat exchanger with air as the heat transfer fluid (HTF). The effect of novel heat exchanger configurations is analyzed and compared with normal helical coil geometry, based on various pitch sizes. Furthermore, the operating parameters of MH storage and HTF are numerically investigated to obtain optimal values. ANSYS Fluent 2020 R2 is utilized for the numerical simulations. Results from this study demonstrate that MH storage performance is significantly improved by using a semi-cylindrical coil heat exchanger (SCHE). The hydrogen absorption duration reduces by 59% compared to a normal helical coil heat exchanger. The lowest coil pitch from SCHE leads to a 61% reduction of the absorption time. In terms of operating parameters for the MH storage with SCHE, all selected parameters provide a major improvement in the hydrogen absorption process, especially the inlet temperature of the HTF.

Abstract Oxygen vacancies (V o) in electrocatalysts are closely correlated with the hydrogen evolution reaction (HER) activity. The role of vacancy defects and the effect of their concentration, however, yet remains unclear. Herein, Bi2O3, an unfavorable electrocatalyst for the HER due to a less than ideal hydrogen adsorption Gibbs free energy (ΔG H*), is utilized as a perfect model to explore the function of V o on HER performance. Through a facile plasma irradiation strategy, Bi2O3 nanosheets with different V o concentrations are fabricated to evaluate the influence of defects on the HER process. Unexpectedly, while the generated oxygen vacancies contribute to the enhanced HER performance, higher V o concentrations beyond a saturation value result in a significant drop in HER activity. By tunning the V o concentration in the Bi2O3 nanosheets via adjusting the treatment time, the Bi2O3 catalyst with an optimized oxygen vacancy concentration and detectable charge carrier concentration of 1.52 × 1024 cm−3 demonstrates enhanced HER performance with an overpotential of 174.2 mV to reach 10 mA cm−2, a Tafel slope of 80 mV dec−1, and an exchange current density of 316 mA cm−2 in an alkaline solution, which approaches the top-tier activity among Bi-based HER electrocatalysts. Density-functional theory calculations confirm the preferred adsorption of H* onto Bi2O3 as a function of oxygen chemical potential (∆μ O) and oxygen partial potential (P O2) and reveal that high V o concentrations result in excessive stability of adsorbed hydrogen and hence the inferior HER activity. This study reveals the oxygen vacancy concentration-HER catalytic activity relationship and provides insights into activating catalytically inert materials into highly efficient electrocatalysts.

Abstract Topological materials have been recently regarded as ideal catalysts for heterogeneous reactions due to their surface metallic states and high carrier mobility. However, the underlying relationship between their catalytic performance and topological states is under debate. It has been discovered that the electride 12CaO·7Al2O3 (C12A7:4e−) hosts multifold fermions and Fermi arcs on the (001) surface near the Fermi level due to the interstitial electrons. Through the comparison of catalytic performance under different doping and strain conditions, based on the hydrogen evolution process, it has been demonstrated that the excellent catalytic performance indeed originates from topological properties. A linear relationship between the length of Fermi arcs, and Gibbs free energy (ΔGH*) has been found, which not only provides the direct evidence to link the enhanced catalytic performance and surface Fermi arc states, but also fully clarifies the fundamental mechanism in topological catalysis.

Medical image classification poses significant challenges in real-world scenarios. One major obstacle is the scarcity of labelled training data, which hampers the performance of image-classification algorithms and generalisation. Gathering sufficient labelled data is often difficult and time-consuming in the medical domain, but deep learning (DL) has shown remarkable performance, although it typically requires a large amount of labelled data to achieve optimal results. Transfer learning (TL) has played a pivotal role in reducing the time, cost, and need for a large number of labelled images. This paper presents a novel TL approach that aims to overcome the limitations and disadvantages of TL that are characteristic of an ImageNet dataset, which belongs to a different domain. Our proposed TL approach involves training DL models on numerous medical images that are similar to the target dataset. These models were then fine-tuned using a small set of annotated medical images to leverage the knowledge gained from the pre-training phase. We specifically focused on medical X-ray imaging scenarios that involve the humerus and wrist from the musculoskeletal radiographs (MURA) dataset. Both of these tasks face significant challenges regarding accurate classification. The models trained with the proposed TL were used to extract features and were subsequently fused to train several machine learning (ML) classifiers. We combined these diverse features to represent various relevant characteristics in a comprehensive way. Through extensive evaluation, our proposed TL and feature-fusion approach using ML classifiers achieved remarkable results. For the classification of the humerus, we achieved an accuracy of 87.85%, an F1-score of 87.63%, and a Cohen’s Kappa coefficient of 75.69%. For wrist classification, our approach achieved an accuracy of 85.58%, an F1-score of 82.70%, and a Cohen’s Kappa coefficient of 70.46%. The results demonstrated that the models trained using our proposed TL approach outperformed those trained with ImageNet TL. We employed visualisation techniques to further validate these findings, including a gradient-based class activation heat map (Grad-CAM) and locally interpretable model-independent explanations (LIME). These visualisation tools provided additional evidence to support the superior accuracy of models trained with our proposed TL approach compared to those trained with ImageNet TL. Furthermore, our proposed TL approach exhibited greater robustness in various experiments compared to ImageNet TL. Importantly, the proposed TL approach and the feature-fusion technique are not limited to specific tasks. They can be applied to various medical image applications, thus extending their utility and potential impact. To demonstrate the concept of reusability, a computed tomography (CT) case was adopted. The results obtained from the proposed method showed improvements.

Electroreduction of CO2 into chemical fuels holds promise for mitigating environmental pollution and energy crisis. This work presents a distinct design of ferroelectric catalysts with high catalytic activity and selectivity for efficient and controllable electrochemical CO2 reduction reaction.

Detection of early clinical keratoconus (KCN) is a challenging task, even for expert clinicians. In this study, we propose a deep learning (DL) model to address this challenge. We first used Xception and InceptionResNetV2 DL architectures to extract features from three different corneal maps collected from 1371 eyes examined in an eye clinic in Egypt. We then fused features using Xception and InceptionResNetV2 to detect subclinical forms of KCN more accurately and robustly. We obtained an area under the receiver operating characteristic curves (AUC) of 0.99 and an accuracy range of 97–100% to distinguish normal eyes from eyes with subclinical and established KCN. We further validated the model based on an independent dataset with 213 eyes examined in Iraq and obtained AUCs of 0.91–0.92 and an accuracy range of 88–92%. The proposed model is a step toward improving the detection of clinical and subclinical forms of KCN.

Carbon nanothreads are promising for applications in mechanical energy storage and energy harvesting. Here the authors use large-scale molecular dynamics simulations and continuum elasticity theory to explore mechanical energy storage in carbon nanothreads-based bundles.

Drying is a complex process of simultaneous heat, mass, and momentum transport phenomena with continuous phase changes. Numerical modelling is one of the most effective tools to mechanistically express the different physics of drying processes for accurately predicting the drying kinetics and understanding the morphological changes during drying. However, the mathematical modelling of drying processes is complex and computationally very expensive due to multiphysics and the multiscale nature of heat and mass transfer during drying. Physics-informed machine learning (PIML)-based modelling has the potential to overcome these drawbacks and could be an exciting new addition to drying research for describing drying processes by embedding fundamental transport laws and constraints in machine learning models. To develop such a novel PIML-based model for drying applications, it is necessary to have a fundamental understanding of heat, mass, and momentum transfer processes and their mathematical formulation of drying processes, in addition to data-driven modelling knowledge. Based on a comprehensive literature review, this paper presents two types of information: fundamental physics-based information about drying processes and data-driven modelling strategies to develop PIML-based models for drying applications. The current status of physics-based models and PIML-based models and their limitations are discussed. A sample PIML-based modelling framework for drying application is presented. Finally, the challenges of addressing simultaneous heat, mass, and momentum transport phenomena in PIML modelling for optimizing the drying process are presented at the end of this paper. It is expected that the information in this manuscript will be beneficial for further advancing the field.

Biodiesel is an alternative source of fuel for various automotive applications. Because of the increasing demand for energy and the scarcity of fossil fuels, researchers have turned their attention to biodiesel production from various sources in recent years. The production of biofuels from organic materials and waste components allows for the use of these waste resources in transporting resources and people over long distances. As a result, developing sustainable measures for this aspect of life is critical, as knowledge of appropriate fuel sources, corresponding emissions, and health impacts will benefit the environment and public health assessment, which is currently lacking in the literature. This study investigates biodiesel’s composition and production process, in addition to biodiesel emissions and their associated health effects. Based on the existing literature, a detailed analysis of biodiesel production from vegetable oil crops and emissions was undertaken. This study also considered vegetable oil sources, such as food crops, which can have a substantial impact on the environment if suitable growing procedures are not followed. Incorporating biodegradable fuels as renewable and sustainable solutions decreases pollution to the environment. The effects of biodiesel exhaust gas and particulates on human health were also examined. According to epidemiologic studies, those who have been exposed to diesel exhaust have a 1.2–1.5 times higher risk of developing lung cancer than those who have not. In addition, for every 24 parts per billion increase in NO2 concentration, symptom prevalence increases 2.7-fold. Research also suggests that plain biodiesel combustion emissions are more damaging than petroleum diesel fuel combustion emissions. A comprehensive analysis of biodiesel production, emissions, and health implications would advance this field’s understanding.

The main challenge of the health risk assessment of the aerosol transport and deposition to the lower airways is the high computational cost. A standard large-scale airway model needs a week to a month of computational time in a high-performance computing system. Therefore, developing an innovative tool that accurately predicts transport behaviour and reduces computational time is essential. This study aims to develop a novel and innovative machine learning (ML) model to predict particle deposition to the lower airways. The first-ever study uses ML techniques to explore the pulmonary aerosol TD in a digital 17-generation airway model. The ML model uses the computational data for a 17-generation airway model and four standard ML regression models are used to save the computational cost. Random forest (RF), k-nearest neighbour (k-NN), multi-layer perceptron (MLP) and Gaussian process regression (GPR) techniques are used to develop the ML models. The MLP regression model displays more accurate estimates than other ML models. Finally, a prediction model is developed, and the results are significantly closer to the measured values. The prediction model predicts the deposition efficiency (DE) for different particle sizes and flow rates. A comprehensive lobe-specific DE is also predicted for various flow rates. This first-ever aerosol transport prediction model can accurately predict the DE in different regions of the airways in a couple of minutes. This innovative approach and accurate prediction will improve the literature and knowledge of the field.

Airway stenosis is a global respiratory health problem that is caused by airway injury, endotracheal intubation, malignant tumor, lung aging, or autoimmune diseases. A precise understanding of the airflow dynamics and pharmaceutical aerosol transport through the multi-stenosis airways is vital for targeted drug delivery, and is missing from the literature. The object of this study primarily relates to behaviors and nanoparticle transport through the multi-stenosis sections of the trachea and upper airways. The combination of a CT-based mouth–throat model and Weibel’s model was adopted in the ANSYS FLUENT solver for the numerical simulation of the Euler–Lagrange (E-L) method. Comprehensive grid refinement and validation were performed. The results from this study indicated that, for all flow rates, a higher velocity was usually found in the stenosis section. The maximum velocity was found in the stenosis section having a 75% reduction, followed by the stenosis section having a 50% reduction. Increasing flow rate resulted in higher wall shear stress, especially in stenosis sections. The highest pressure was found in the mouth–throat section for all flow rates. The lowest pressure was usually found in stenosis sections, especially in the third generation. Particle escape rate was dependent on flow rate and inversely dependent on particle size. The overall deposition efficiency was observed to be significantly higher in the mouth–throat and stenosis sections compared to other areas. However, this was proven to be only the case for a particle size of 1 nm. Moreover, smaller nanoparticles were usually trapped in the mouth–throat section, whereas larger nanoparticle sizes escaped through the lower airways from the left side of the lung; this accounted for approximately 50% of the total injected particles, and 36% escaped from the right side. The findings of this study can improve the comprehensive understanding of airflow patterns and nanoparticle deposition. This would be beneficial in work with polydisperse particle deposition for treatment of comprehensive stenosis with specific drugs under various disease conditions.

The excellent mechanical properties of graphyne (GY) have made it an appealing candidate in the field of impact protection. We assessed the deformation mechanisms of monolayer GY nanosheets of different morphologies, including α-GY, β-GY, γ-GY and 6612-GY, under supersonic-velocity impacts (from 1 to 6 km/s) based on in silico studies. Generally, cracks initiate at the geometry center and the nanosheet experiences significant out-of-plane deformation before the propagation of cracks. Tracking the atomic von Mises stress distribution, it is found that its cumulative density function has a strong correlation with the magnitude of the Young’s modulus of the GYs. For nanosheets with a higher Young’s modulus, it tends to transfer momentum at a faster rate. Thus, a better energy dissipation or delocalization is expected during impact. This study provides a fundamental understanding of the deformation and penetration mechanisms of monolayer GY nanosheets under impact, which is crucial in order to facilitate their emerging applications for impact protection.

Abstract As a typical class of single‐atom catalysts (SACs) possessing prominent advantages of high reactivity, high selectivity, high stability, and maximized atomic utilization, emerging metal‐nitrogen‐doped carbon (M‐N‐C) materials, wherein dispersive metal atoms are coordinated to nitrogen atoms doped in carbon nanomaterials, have presented a high promise to replace the conventional metal or metal oxides‐based catalysts. In this work, recent progress in M‐N‐C‐based materials achieved in both theoretical and experimental investigations is summarized and general principles for novel catalysts design from electronic structure modulating are provided. Firstly, the applications and mechanisms on the advantages and challenges of M‐N‐C‐based materials for a variety of sustainable fuel generation and bioinspired reactions, including the oxygen reduction reaction (ORR), oxygen evolution reaction (OER), hydrogen evolution reaction (HER), carbon dioxide reduction reaction (CO2RR), nitrogen reduction reaction (NRR), and nanozyme reactions are reviewed. Then, strategies toward enhancing the catalytic performance by engineering the nature of metal ion centers, coordinative environment of active centers, carbon support, and their synergistic cooperation, are proposed. Finally, prospects for the rational design of next generation high‐performance M‐N‐C‐based catalysts are outlined. It is expected that this work will provide insights into high‐performance catalysts innovation for sustainable and environmental technologies.

Atherosclerotic plaque rupture is a catastrophic event that contributes to mortality and long-term disability. A better understanding of the plaque mechanical behavior is essential for the identification of vulnerable plaques pre-rupture. Plaque is subjected to a natural dynamic mechanical environment under hemodynamic loading. Therefore, it is important to understand the mechanical response of plaque tissue under cyclic loading conditions. Moreover, experimental data of such mechanical properties are fundamental for more clinically relevant biomechanical modeling and numerical simulations for risk stratification. This study aims to experimentally and numerically characterize the stress-relaxation and cyclic mechanical behavior of carotid plaque tissue. Instron microtester equipped with a custom-developed setup was used for the experiments. Carotid plaque samples excised at endarterectomy were subjected to uniaxial tensile, stress-relaxation, and cyclic loading protocols. Thirty percent of the underlying load level obtained from the uniaxial tensile test results was used to determine the change in mechanical properties of the tissue over time under a controlled testing environment (Control tests). The stress-relaxation test data was used to calibrate the hyperelastic (neo-Hookean, Ogden, Yeoh) and linear viscoelastic (Prony series) material parameters. The normalized relaxation force increased initially and slowly stabilized toward the end of relaxation phase, highlighting the viscoelastic behavior. During the cyclic tests, there was a decrease in the peak force as a function of the cycle number indicating mechanical distension due to repeated loading that varied with different frequencies. The material also accumulated residual deformation, which increased with the cycle number. This trend showed softening behavior of the samples. The results of this preliminary study provide an enhanced understanding of in vivo stress-relaxation and cyclic behavior of the human atherosclerotic plaque tissue.

A patient-specific carotid bifurcation with tandem stenosis found at both internal carotid artery (ICA) and common carotid artery (CCA) was studied. The in vivo pre-carotid endarterectomy (pre-CEA) multi-spectral magnetic resonance imaging (MRI) were performed and in vitro post-CEA carotid plaque tissue sample was collected. MR imaging data and tissue sample staining histology were used to recognize the plaque components. Further, the computational fluid dynamics (CFD) were performed on four MR-based reconstructed 3D carotid bifurcation models (the patient-specific geometry with tandem stenosis and three presumptive geometries by removing the stenosis part). The flow and shear stress behavior affected by the tandem stenosis was analyzed. From the results of MR segmentation and histology analysis, plaque lipid pool and calcification were found at both ICA and CCA. From the result of CFD simulation, the flow shear stress behavior suggested the tandem stenosis as a more “dangerous” situation than a single-stenosis artery. Besides, the CFD results deduced that the stenosis at the CCA location formed initially and led to the subsequent formation of stenosis at ICA. This study suggests that when planning CEA, CFD simulation on the presumptive models could help clinicians to estimate the blood flow behavior after surgery. Particular attention should be paid to the case of tandem stenosis, as the local hemodynamic environment is more complex and treatment of one stenosis may lead to a variation in the hemodynamic loading on the second plaque, which may result in either a higher risk of plaque rupture or restenosis.

Most chiral molecules and structures in living organisms exist as single enantiomers, but why? Here, the authors investigated surface-mediated homochirality on the single-molecule level and show that it can be triggered by the chirality unbalance of two adsorption configuration monomers.

Abstract Background Red blood cells (RBCs) deform significantly and repeatedly when passing through narrow capillaries and delivering dioxygen throughout the body. Deformability of RBCs is a key characteristic, largely governed by the mechanical properties of the cell membrane. This study investigated RBC mechanical properties using atomic force microscopy (AFM) with the aim to develop a coarse-grained particle method model to study for the first time RBC indentation in both 2D and 3D. This new model has the potential to be applied to further investigate the local deformability of RBCs, with accurate control over adhesion, probe geometry and position of applied force. Results The model considers the linear stretch capacity of the cytoskeleton, bending resistance and areal incompressibility of the bilayer, and volumetric incompressibility of the internal fluid. The model’s performance was validated against force–deformation experiments performed on RBCs under spherical AFM indentation. The model was then used to investigate the mechanisms which absorbed energy through the indentation stroke, and the impact of varying stiffness coefficients on the measured deformability. This study found the membrane’s bending stiffness was most influential in controlling RBC physical behaviour for indentations of up to 200 nm. Conclusions As the bilayer provides bending resistance, this infers that structural changes within the bilayer are responsible for the deformability changes experienced by deteriorating RBCs. The numerical model presented here established a foundation for future investigations into changes within the membrane that cause differences in stiffness between healthy and deteriorating RBCs, which have already been measured experimentally with AFM.

3D Printed biodegradable polymeric scaffolds are critical to repair a bone defect, which can provide the individual porous and network microenvironments for cell attachment and bone tissue regeneration. Biodegradable PCL/HA composites were prepared with the blending of poly(ε-caprolactone) (PCL) and hydroxyapatite nanoparticles (HA). Subsequently, the PCL/HA scaffolds were produced by the melting deposition-forming method using PCL/HA composites as the raw materials in this work. Through a serial of in vitro assessments, it was found that the PCL/HA composites possessed good biodegradability, low cell cytotoxicity, and good biocompatibility, which can improve the cell proliferation of osteoblast cells MC3T3-E1. Meanwhile, in vivo experiments were carried out for the rats with skull defects and rabbits with bone defects. It was observed that the PCL/HA scaffolds allowed the adhesion and penetration of bone cells, which enabled the growth of bone cells and bone tissue regeneration. With a composite design to load an anticancer drug (doxorubicin, DOX) and achieve sustained drug release performance, the multifunctional 3D printed PCL/HA/DOX scaffolds can enhance bone repair and be expected to inhibit probably the tumor cells after malignant bone tumor resection. Therefore, this work signifies that PCL/HA composites can be used as the potential biodegradable scaffolds for bone repairing.

Carbon fibres are emerging as a promising material for multifunctional nanotextiles. Here, the authors show that diamond nanothread possesses excellent torsional deformation capability and interfacial load transfer efficiency, ideal for constructing next generation carbon fibres.

An improved red blood cell (RBC) membrane model is developed based on the bilayer coupling model (BCM) to accurately predict the complete sequence of stomatocyte-discocyte-echinocyte (SDE) transformation of a RBC. The coarse-grained (CG)-RBC membrane model is proposed to predict the minimum energy configuration of the RBC from the competition between lipid-bilayer bending resistance and cytoskeletal shear resistance under given reference constraints. In addition to the conventional membrane surface area, cell volume and bilayer-leaflet-area-difference constraints, a new constraint: total-membrane-curvature is proposed in the model to better predict RBC shapes in agreement with experimental observations. A quantitative evaluation of several cellular measurements including length, thickness and shape factor, is performed for the first time, between CG-RBC model predicted and three-dimensional (3D) confocal microscopy imaging generated RBC shapes at equivalent reference constraints. The validated CG-RBC membrane model is then employed to investigate the effect of reduced cell volume and elastic length scale on SDE transformation, to evaluate the RBC deformability during SDE transformation, and to identify the most probable RBC cytoskeletal reference state. The CG-RBC membrane model can predict the SDE shape behaviour under diverse shape-transforming scenarios, in-vitro RBC storage, microvascular circulation and flow through microfluidic devices.

Storage lesion is a critical issue facing transfusion treatments, and it adversely affects the quality and viability of stored red blood cells (RBCs). RBC deformability is a key indicator of cell health. Deformability measurements of each RBC unit are a key challenge in transfusion medicine research and clinical haematology. In this paper, a numerical study, inspired from the previous research for RBC deformability and morphology predictions, is conducted for the first time, to investigate the deformability and morphology characteristics of RBCs undergoing storage lesion. This study investigates the evolution of the cell shape factor, elongation index and membrane spicule details, where applicable, of discocyte, echinocyte I, echinocyte II, echinocyte III and sphero-echinocyte morphologies during 42 days of in-vitro storage at 4 °C in saline-adenine-glucose-mannitol (SAGM). Computer simulations were performed to investigate the influence of storage lesion-induced membrane structural defects on cell deformability and its recoverability during optical tweezers stretching deformations. The predicted morphology and deformability indicate decreasing quality and viability of stored RBCs undergoing storage lesion. The loss of membrane structural integrity due to the storage lesion further degrades the cell deformability and recoverability during mechanical deformations. This numerical approach provides a potential framework to study the RBC deformation characteristics under varying pathophysiological conditions for better diagnostics and treatments.

Based on its enticing properties, graphene has been envisioned with applications in the area of electronics, photonics, sensors, bio-applications and others. To facilitate various applications, doping has been frequently used to manipulate the properties of graphene. Despite a number of studies conducted on doped graphene regarding its electrical and chemical properties, the impact of doping on the mechanical properties of graphene has been rarely discussed. A systematic study of the vibrational properties of graphene doped with nitrogen and boron is performed by means of a molecular dynamics simulation. The influence from different density or species of dopants has been assessed. It is found that the impacts on the quality factor, Q, resulting from different densities of dopants vary greatly, while the influence on the resonance frequency is insignificant. The reduction of the resonance frequency caused by doping with boron only is larger than the reduction caused by doping with both boron and nitrogen. This study gives a fundamental understanding of the resonance of graphene with different dopants, which may benefit their application as resonators.

Recently, the capture and storage of CO2 have attracted research interest as a strategy to reduce the global emissions of greenhouse gases. It is crucial to find suitable materials to achieve an efficient CO2 capture. Here we report our study of CO2 adsorption on boron-doped C60 fullerene in the neutral state and in the 1e−-charged state. We use first principle density functional calculations to simulate the CO2 adsorption. The results show that CO2 can form weak interactions with the BC59 cage in its neutral state and the interactions can be enhanced significantly by introducing an extra electron to the system.

Doping is an effective approach that allows for the intrinsic modification of the electrical and chemical properties of nanomaterials. Recently, a graphene and carbon nanotube hybrid structure (GNHS) has been reported, which extends the excellent properties of carbon-based materials to three dimensions. In this paper, we carried out a first-time investigation on the tensile properties of the hybrid structures with different dopants. It is found that with the presence of dopants, the hybrid structures usually exhibit lower yield strength, Young’s modulus, and earlier yielding compared to that of a pristine hybrid structure. For dopant concentrations below 2.5% no significant reduction of Young’s modulus or yield strength could be observed. For all considered samples, the failure is found to initiate at the region where the nanotubes and graphene sheets are connected. After failure, monatomic chains are normally observed around the failure region. Dangling graphene layers without the separation of a residual CNT wall are found to adhere to each other after failure with a distance of about 3.4 Å. This study provides a fundamental understanding of the tensile properties of the doped graphene–nanotube hybrid structures, which will benefit the design and also the applications of graphene-based hybrid materials.

A model of crosslinker unbinding is implemented in a highly coarse-grained granular model of F-actin cytoskeleton. We employ this specific granular model to study the mechanisms of the compressive responses of F-actin networks. It is found that the compressive response of F-actin cytoskeleton has dependency on the strain rate. The evolution of deformation energy in the network indicates that crosslinker unbinding events can induce the remodelling of F-actin cytoskeleton in response to external loadings. The internal stress in F-actin cytoskeleton can efficiently dissipate with the help of crosslinker unbinding, which could lead to the spontaneous relaxation of living cells.

Based on atomic force microscopytechnique, we found that the chondrocytes exhibits stress relaxation behavior. We explored the mechanism of this stress relaxation behavior and concluded that the intracellular fluid exuding out from the cells during deformation plays the most important role in the stress relaxation. We applied the inverse finite element analysis technique to determine necessary material parameters for porohyperelastic (PHE) model to simulate stress relaxation behavior as this model is proven capable of capturing the non-linear behavior and the fluid-solid interaction during the stress relaxation of the single chondrocytes. It is observed that PHE model can precisely capture the stress relaxation behavior of single chondrocytes and would be a suitable model for cell biomechanics.

Semi-solid processing (SSP) is a popular near-net-shape forming technology for metals, while its application is still limited in titanium alloy mainly due to its low formability. Recent works showed that SSP could effectively enhance the formability and mechanical properties of titanium alloys. The processing parameters such as temperature and forging rate/ratio, are directly correlated with the microstructure, which endow the alloy with different chemical and physical properties. Specifically, as a key structural material for the advanced aero-engine, the burn resistant performance is a crucial requirement for the burn resistant titanium alloy. Thus, this work aims to assess the burning behavior of Ti14, a kind of burn resistant alloy, as forged at different semi-solid forging temperatures. The burning characteristics of the alloy are analyzed by a series of burning tests with different burning durations, velocities, and microstructures of burned sample. The results showed that the burning process is highly dependent on the forging temperature, due to the fact that higher temperatures would result in more Ti2Cu precipitate within grain and along grain boundaries. Such a microstructure hinders the transport of oxygen in the stable burning stage through the formation of a kind of oxygen isolation Cu-enriched layer under the burn product zone. This work suggests that the burning resistance of the alloy can be effectively tuned by controlling the temperature during the semi-solid forging process.