In-silico simulation of nanoindentation on bone using a 2D cohesive finite element model.

This study proposed and validated a 2D finite element (FE) model for conducting in-silico simulations of in-situ nanoindentation tests on mineralized collagen fibrils (MCF) and the extrafibrillar matrix (EFM) within human cortical bone. Initially, a multiscale cohesive FE model was developed by adapting a previous model of bone lamellae, encompassing both MCF and EFM. Subsequently, nanoindentation tests were simulated in-silico using this model, and the resulting predictions were compared to AFM nanoindentation test data to verify the model's accuracy. The FE model accurately predicted nanoindentation results under wet conditions, closely aligning with outcomes obtained from AFM nanoindentation tests. Specifically, it successfully mirrored the traction/separation curve, nanoindentation modulus, plastic energy dissipation, and plastic energy ratio obtained from AFM nanoindentation tests. Additionally, this in-silico model demonstrated its ability to capture alterations in nanoindentation properties caused by the removal of bound water, by considering corresponding changes in mechanical properties of the collagen phase and the interfaces among bone constituents. Notably, significant changes in the elastic modulus and plastic energy dissipation were observed in both MCF and EFM compartments of bone, consistent with observations in AFM nanoindentation tests. These findings indicate that the proposed in-silico model effectively captures the influence of ultrastructural changes on bone's mechanical properties at sub-lamellar levels. Presently, no experimental methods exist to conduct parametric studies elucidating the ultrastructural origins of bone tissue fragility. The introduction of this in-silico model presents an invaluable tool to bridge this knowledge gap in the future.

Dynamic Bond-Directed Synthesis of Stable Mesoporous Metal-Organic Frameworks under Room Temperature.

Stable metal-organic frameworks (MOFs) with mesopores (2-50 nm) are promising platforms for immobilizing nanosized functional compounds, such as metal-oxo clusters, metal-sulfide quantum dots, and coordination complexes. However, these species easily decompose under acidic conditions or high temperatures, hindering their in situ encapsulation in stable MOFs, which are usually synthesized under harsh conditions involving excess acid modulators and high temperatures. Herein, we report a route for the room-temperature and acid-modulator-free synthesis of stable mesoporous MOFs and MOF catalysts with acid-sensitive species encapsulated: (1) we initially construct a MOF template by connecting stable Zr6 clusters with labile Cu-bipyridyl moieties; (2) Cu-bipyridyl moieties are subsequently exchanged by organic linkers to afford a stable version of Zr-MOFs; (3) acid-sensitive species, including polyoxometalates (POMs), CdSeS/ZnS quantum dots, and Cu-coordination cages, can be encapsulated in situ into the MOFs during step 1. The room-temperature synthesis allows the isolation of mesoporous MOFs with 8-connected Zr6 clusters and reo topology as kinetic products, which are inaccessible by traditional solvothermal synthesis. Furthermore, acid-sensitive species remain stable, active, and locked within the frameworks during MOF synthesis. We observed high catalytic activity for VX degradation by the POM@Zr-MOF catalysts as a result of the synergy between redox-active POMs and Lewis-acidic Zr sites. The dynamic bond-directed method will accelerate the discovery of large-pore stable MOFs and offer a mild route to avoid the decomposition of catalysts during MOF synthesis.

Surface passivation of intensely luminescent all-inorganic nanocrystals and their direct optical patterning.

All-inorganic nanocrystals (NCs) are of great importance in a range of electronic devices. However, current all-inorganic NCs suffer from limitations in their optical properties, such as low fluorescence efficiencies. Here, we develop a general surface treatment strategy to obtain intensely luminescent all-inorganic NCs (ILANs) by using designed metal salts with noncoordinating anions that play a dual role in the surface treatment process: (i) removing the original organic ligands and (ii) binding to unpassivated Lewis basic sites to preserve the photoluminescent (PL) properties of the NCs. The absolute photoluminescence quantum yields (PLQYs) of red-emitting CdSe/ZnS NCs, green-emitting CdSe/CdZnSeS/ZnS NCs and blue-emitting CdZnS/ZnS NCs in polar solvents are 97%, 80% and 72%, respectively. Further study reveals that the passivated Lewis basic sites of ILANs by metal cations boost the efficiency of radiative recombination of electron-hole pairs. While the passivation of Lewis basic sites leads to a high PLQY of ILANs, the exposed Lewis acidic sites provide the possibility for in situ tuning of the functions of NCs, creating opportunities for direct optical patterning of functional NCs with high resolution.

Designing inorganically functionalized magic-size II-VI clusters and unraveling their surface states.

Surface engineering is a critical step in the functionalization of nanomaterials to improve their optical and electrochemical properties. However, this process remains a challenge in II-VI magic-size clusters (MSCs) due to their high sensitivity to the environment. Herein, we developed a general surface modification strategy to design all-inorganic MSCs by using certain metal salts (cation = Zn2+, In3+; Anion = Cl-, NO3 -, OTf-) and stabilized (CdS)34, (CdSe)34 and (ZnSe)34 MSCs in polar solvents. We further investigated the surface states of II-VI MSCs using electrochemiluminescence (ECL). The mechanism study revealed that the ECL emission was attributed to . Two ECL emissions at 556 nm and 530 nm demonstrated two surface passivation modes on (CdS)34 MSCs, which can be tuned by the surface ligands. The achievement of surface engineering opens a new design space for functional MSC compounds.

Probability-based approach for characterization of microarchitecture and its effect on elastic properties of trabecular bone.

Trabecular bone is a random cellular solid with an interconnected network of plate-like and rod-like components. However, the structural randomness and complexity have hindered rigorous mathematical modeling of trabecular bone microarchitecture. Recent advancements in imaging processing techniques have enabled us to define the size, orientation, and spatial location of individual trabecular plates and rods in trabecular bone. Based on the essential information, this study proposed a probability-based approach to define the size, orientation, and spatial distributions of trabecular plates and rods for trabecular bone cubes (N = 547) acquired from six human cadaver proximal femurs. Using two groups of probability-based parameters, it was attempted to capture microarchitectural details, which could not be captured by the existing histomorphometric parameters, but crucial to the elastic properties of trabecular bone. The elastic properties of the trabecular bone cubes in three principal axes were estimated using microCT based finite element (FE) simulations. Based on the results of multivariate multiple regression modeling, the efficacy of the two groups of probability-based parameters in prediction of the elastic properties was verified in comparison with that of the existing histomorphometric parameters (BV/TV, Tb.Th, Tb.Sp, DA, EF.Med, and Conn.D). The results indicated that the regression models trained using the probability-based parameters had a comparable and even better accuracy (rMSE = 0.621 and 0.548) than that of the histomorphometric parameters (rMSE = 0.647). More importantly, the probability-based parameters could provide more insights into some unexplored microarchitectural features, such as individual trabecular size, orientation, and spatial distributions, which are also critical to the elastic properties of trabecular bone.

Can DXA image-based deep learning model predict the anisotropic elastic behavior of trabecular bone?

3D image-based finite element (FE) and bone volume fraction (BV/TV)/fabric tensor modeling techniques are currently used to determine the apparent stiffness tensor of trabecular bone for assessing its anisotropic elastic behavior. Inspired by the recent success of deep learning (DL) techniques, we hypothesized that DL modeling techniques could be used to predict the apparent stiffness tensor of trabecular bone directly using dual-energy X-ray absorptiometry (DXA) images. To test the hypothesis, a convolutional neural network (CNN) model was trained and validated to predict the apparent stiffness tensor of trabecular bone cubes using their DXA images. Trabecular bone cubes obtained from human cadaver proximal femurs were used to obtain simulated DXA images as input, and the apparent stiffness tensor of the trabecular cubes determined by using micro-CT based FE simulations was used as output (ground truth) to train the DL model. The prediction accuracy of the DL model was evaluated by comparing it with the micro-CT based FE models, histomorphometric parameter based multiple linear regression models, and BV/TV/fabric tensor based multiple linear regression models. The results showed that DXA image-based DL model achieved high fidelity in predicting the apparent stiffness tensor of trabecular bone cubes (R2 = 0.905-0.973), comparable to or better than the histomorphometric parameter based multiple linear regression and BV/TV/fabric tensor based multiple linear regression models, thus supporting the hypothesis of this study. The outcome of this study could be used to help develop DXA image-based DL techniques for clinical assessment of bone fracture risk.

Removal of glycosaminoglycans affects the in situ mechanical behavior of extrafibrillar matrix in bone.

Previous studies have shown that glycosaminoglycans (GAGs) in bone matrix, coupling with water in bone matrix, may play a significant role in toughening bone tissues. Since GAGs are most likely present only in the extrafibrillar matrix (EFM) of bone, we hypothesized that GAGs in EFM would have a major impact on bone tissue toughness. To confirm this conjecture, we removed GAGs ex vivo from human cadaveric bone samples using a protein deglycosylation mix kit and then examined the in situ mechanical behavior of mineralized collagen fibrils (MCFs) and the surrounding EFM of the samples, using a high-resolution atomic force microscopy (AFM). By testing the bone samples before and after removal of GAGs, we found that under the wet condition removal of GAGs resulted in an increase in the elastic modulus of both EFM and MCFs, whereas a significant decrease in plastic energy dissipation was observed mainly in EFM. In contrast, under the dry condition the removal of GAGs had little effects on the mechanical properties of either MCFs or EFM. These results suggest that both MCFs and EFM contribute to the plastic energy dissipation of bone, whereas in the presence of matrix water removal of GAGs significantly reduces the capacity of EFM in plastic energy dissipation, but not MCFs. In addition, GAGs may affect the elastic modulus of both EFM and MCFs. These findings give rise to new understanding to the underlying mechanism of GAGs in toughening of bone tissues.

Prediction of trabecular bone architectural features by deep learning models using simulated DXA images.

Dual-energy X-ray absorptiometry (DXA) is widely used for clinical assessment of bone mineral density (BMD). Recent evidence shows that DXA images may also contain microstructural information of trabecular bones. However, no current image processing techniques could aptly extract the information. Inspired by the success of deep learning techniques in medical image analyses, we hypothesized in this study that DXA image-based deep learning models could predict the major microstructural features of trabecular bone with a reasonable accuracy. To test the hypothesis, 1249 trabecular cubes (6 mm × 6 mm × 6 mm) were digitally dissected out from the reconstruction of seven human cadaveric proximal femurs using microCT scans. From each cube, simulated DXA images in designated projections were generated, and the histomorphometric parameters (i.e., BV/TV, BS, Tb.Th, DA, Conn. D, and SMI) of the cube were determined using Image J. Convolutional neural network (CNN) models were trained using the simulated DXA images to predict the histomorphometric parameters of trabecular bone cubes. The results exhibited that the CNN models achieved high fidelity in predicting these histomorphometric parameters (from R = 0.80 to R = 0.985), showing that the DL models exhibited the capability of predicting the microstructural features using DXA images. This study also showed that the number and resolution of input simulated DXA images had considerable impacts on the prediction accuracy of the DL models. These findings support the hypothesis of this study and indicate a high potential of using DXA images in prediction of osteoporotic bone fracture risk.

Degradation of polyvinyl chloride microplastics via an electro-Fenton-like system with a TiO2/graphite cathode.

Nowadays, microplastic pollution has been brought into focus for its hazards to aquatic life. However, researches on the electrocatalytic treatment for efficient degradation of microplastics are still insufficient. Herein, an electro-Fenton like (EF-like) technology based on TiO2/graphite (TiO2/C) cathode was put forward to degrade polyvinyl chloride (PVC), a typical microplastic in water. It exhibited a remarkable performance on PVC degradation via cathodic reduction dechlorination and hydroxyl radical (OH) oxidation simultaneously. Besides, the effects of reaction temperature and initial PVC concentration were investigated. Under optimal conditions, the dechlorination efficiency of PVC reached 75 % after potentiostatic electrolysis at -0.7 V vs. Ag/AgCl for 6 h. The intermediate products were explored during the degradation of PVC microplastics. The surface morphologies and molecular weight of PVC changed accordingly. Based on these results, a possible degradation process for PVC was proposed. This work demonstrated that such a heterogeneous EF-like technology using TiO2/C cathode was hopefully to provide an eco-friendly method for microplastic wastewater treatment.

Three-dimensional rendering of trabecular bone microarchitecture using a probabilistic approach.

In the past, researchers have attempted to model trabecular bone using computational techniques. However, only a few of these models are visually similar, but not representative of the microstructural characteristics of real trabecular bones. In this study, we hypothesized that probabilistic modeling approaches could be used to generate representative digital models that capture the microstructural features of real trabecular bones. To test this hypothesis, we proposed a novel mathematical framework to build the digital models and compared the digital models to real bone specimens. First, an initial three-dimensional cellular structure was generated using Voronoi tessellation, with the faces and edges of the Voronoi cells considered as a pool of potential trabecular plates and rods, respectively. Then, inverse Monte Carlo simulations were performed to select, delete, or reassign plates and rods until the underlying size, orientation, and spatial distributions of the plates and rods converged to the target distributions obtained from real trabecular bone microstructures. Next, digital graphics techniques were used to define the thickness of trabecular plates and the diameter of trabecular rods, followed by writing the model into a Standard Tessellation Language file and then smoothing the model surfaces for a more natural appearance. To verify the efficacy of the digital model in capturing the microstructural features of real trabecular bones, forty-six digital models with a large variation in microstructural features were generated based on the target distributions obtained from trabecular bone specimens of twelve human cadaveric femurs. Then, the histomorphological parameters of the digital models were compared with those of the real trabecular bone specimens. The results indicate that the digital models are capable of capturing major microstructural features of the trabecular bone samples, thus proving the hypothesis that the proposed probabilistic modeling approach could render real trabecular bone microstructures.

Fermi level-tuned optics of graphene for attocoulomb-scale quantification of electron transfer at single gold nanoparticles.

Measurement of electron transfer at single-molecule level is normally restricted by the detection limit of faraday current, currently in a picoampere to nanoampere range. Here we demonstrate a unique graphene-based electrochemical microscopy technique to make an advance in the detection limit. The optical signal of electron transfer arises from the Fermi level-tuned Rayleigh scattering of graphene, which is further enhanced by immobilized gold nanostars. Owing to the specific response to surface charged carriers, graphene-based electrochemical microscopy enables an attoampere-scale detection limit of faraday current at multiple individual gold nanoelectrodes simultaneously. Using the graphene-based electrochemical microscopy, we show the capability to quantitatively measure the attocoulomb-scale electron transfer in cytochrome c adsorbed at a single nanoelectrode. We anticipate the graphene-based electrochemical microscopy to be a potential electrochemical tool for in situ study of biological electron transfer process in organelles, for example the mitochondrial electron transfer, in consideration of the anti-interference ability to chemicals and organisms.