Mechanostat parameters estimated from time-lapsed in vivo micro-computed tomography data of mechanically driven bone adaptation are logarithmically dependent on loading frequency.

Mechanical loading is a key factor governing bone adaptation. Both preclinical and clinical studies have demonstrated its effects on bone tissue, which were also notably predicted in the mechanostat theory. Indeed, existing methods to quantify bone mechanoregulation have successfully associated the frequency of (re)modeling events with local mechanical signals, combining time-lapsed in vivo micro-computed tomography (micro-CT) imaging and micro-finite element (micro-FE) analysis. However, a correlation between the local surface velocity of (re)modeling events and mechanical signals has not been shown. As many degenerative bone diseases have also been linked to impaired bone (re)modeling, this relationship could provide an advantage in detecting the effects of such conditions and advance our understanding of the underlying mechanisms. Therefore, in this study, we introduce a novel method to estimate (re)modeling velocity curves from time-lapsed in vivo mouse caudal vertebrae data under static and cyclic mechanical loading. These curves can be fitted with piecewise linear functions as proposed in the mechanostat theory. Accordingly, new (re)modeling parameters can be derived from such data, including formation saturation levels, resorption velocity moduli, and (re)modeling thresholds. Our results revealed that the norm of the gradient of strain energy density yielded the highest accuracy in quantifying mechanoregulation data using micro-finite element analysis with homogeneous material properties, while effective strain was the best predictor for micro-finite element analysis with heterogeneous material properties. Furthermore, (re)modeling velocity curves could be accurately described with piecewise linear and hyperbola functions (root mean square error below 0.2 µm/day for weekly analysis), and several (re)modeling parameters determined from these curves followed a logarithmic relationship with loading frequency. Crucially, (re)modeling velocity curves and derived parameters could detect differences in mechanically driven bone adaptation, which complemented previous results showing a logarithmic relationship between loading frequency and net change in bone volume fraction over 4 weeks. Together, we expect this data to support the calibration of in silico models of bone adaptation and the characterization of the effects of mechanical loading and pharmaceutical treatment interventions in vivo.

Trabecular bone remodeling in the aging mouse: A micro-multiphysics agent-based in silico model using single-cell mechanomics.

Bone remodeling is regulated by the interaction between different cells and tissues across many spatial and temporal scales. Notably, in silico models are regarded as powerful tools to further understand the signaling pathways that regulate this intricate spatial cellular interplay. To this end, we have established a 3D multiscale micro-multiphysics agent-based (micro-MPA) in silico model of trabecular bone remodeling using longitudinal in vivo data from the sixth caudal vertebra (CV6) of PolgA(D257A/D257A) mice, a mouse model of premature aging. Our in silico model includes a variety of cells as single agents and receptor-ligand kinetics, mechanomics, diffusion and decay of cytokines which regulate the cells' behavior. We highlighted its capabilities by simulating trabecular bone remodeling in the CV6 of five mice over 4 weeks and we evaluated the static and dynamic morphometry of the trabecular bone microarchitecture. Based on the progression of the average trabecular bone volume fraction (BV/TV), we identified a configuration of the model parameters to simulate homeostatic trabecular bone remodeling, here named basal. Crucially, we also produced anabolic, anti-anabolic, catabolic and anti-catabolic responses with an increase or decrease by one standard deviation in the levels of osteoprotegerin (OPG), receptor activator of nuclear factor kB ligand (RANKL), and sclerostin (Scl) produced by the osteocytes. Our results showed that changes in the levels of OPG and RANKL were positively and negatively correlated with the BV/TV values after 4 weeks in comparison to basal levels, respectively. Conversely, changes in Scl levels produced small fluctuations in BV/TV in comparison to the basal state. From these results, Scl was deemed to be the main driver of equilibrium while RANKL and OPG were shown to be involved in changes in bone volume fraction with potential relevance for age-related bone features. Ultimately, this micro-MPA model provides valuable insights into how cells respond to their local mechanical environment and can help to identify critical pathways affected by degenerative conditions and ageing.

An in silico micro-multiphysics agent-based approach for simulating bone regeneration in a mouse femur defect model.

Bone defects represent a challenging clinical problem as they can lead to non-union. In silico models are well suited to study bone regeneration under varying conditions by linking both cellular and systems scales. This paper presents an in silico micro-multiphysics agent-based (micro-MPA) model for bone regeneration following an osteotomy. The model includes vasculature, bone, and immune cells, as well as their interaction with the local environment. The model was calibrated by time-lapsed micro-computed tomography data of femoral osteotomies in C57Bl/6J mice, and the differences between predicted bone volume fractions and the longitudinal in vivo measurements were quantitatively evaluated using root mean square error (RMSE). The model performed well in simulating bone regeneration across the osteotomy gap, with no difference (5.5% RMSE, p = 0.68) between the in silico and in vivo groups for the 5-week healing period - from the inflammatory phase to the remodelling phase - in the volume spanning the osteotomy gap. Overall, the proposed micro-MPA model was able to simulate the influence of the local mechanical environment on bone regeneration, and both this environment and cytokine concentrations were found to be key factors in promoting bone regeneration. Further, the validated model matched clinical observations that larger gap sizes correlate with worse healing outcomes and ultimately simulated non-union. This model could help design and guide future experimental studies in bone repair, by identifying which are the most critical in vivo experiments to perform.

Bone Mechanoregulation Allows Subject-Specific Load Estimation Based on Time-Lapsed Micro-CT and HR-pQCT in Vivo.

Patients at high risk of fracture due to metabolic diseases frequently undergo long-term antiresorptive therapy. However, in some patients, treatment is unsuccessful in preventing fractures or causes severe adverse health outcomes. Understanding load-driven bone remodelling, i.e., mechanoregulation, is critical to understand which patients are at risk for progressive bone degeneration and may enable better patient selection or adaptive therapeutic intervention strategies. Bone microarchitecture assessment using high-resolution peripheral quantitative computed tomography (HR-pQCT) combined with computed mechanical loads has successfully been used to investigate bone mechanoregulation at the trabecular level. To obtain the required mechanical loads that induce local variances in mechanical strain and cause bone remodelling, estimation of physiological loading is essential. Current models homogenise strain patterns throughout the bone to estimate load distribution in vivo, assuming that the bone structure is in biomechanical homoeostasis. Yet, this assumption may be flawed for investigating alterations in bone mechanoregulation. By further utilising available spatiotemporal information of time-lapsed bone imaging studies, we developed a mechanoregulation-based load estimation (MR) algorithm. MR calculates organ-scale loads by scaling and superimposing a set of predefined independent unit loads to optimise measured bone formation in high-, quiescence in medium-, and resorption in low-strain regions. We benchmarked our algorithm against a previously published load history (LH) algorithm using synthetic data, micro-CT images of murine vertebrae under defined experimental in vivo loadings, and HR-pQCT images from seven patients. Our algorithm consistently outperformed LH in all three datasets. In silico-generated time evolutions of distal radius geometries (n = 5) indicated significantly higher sensitivity, specificity, and accuracy for MR than LH (p < 0.01). This increased performance led to substantially better discrimination between physiological and extra-physiological loading in mice (n = 8). Moreover, a significantly (p < 0.01) higher association between remodelling events and computed local mechanical signals was found using MR [correct classification rate (CCR) = 0.42] than LH (CCR = 0.38) to estimate human distal radius loading. Future applications of MR may enable clinicians to link subtle changes in bone strength to changes in day-to-day loading, identifying weak spots in the bone microstructure for local intervention and personalised treatment approaches.

Localization of light induced in ordered colloidal suspensions: powerful sensing tools.

We study the light-matter coupling by Raman scattering in colloidal suspensions composed by core-shell TiO2@Silica (Rutile@Silica) nanoparticles suspended in ethanol and water solutions. Strong enhancement of the Raman signal per particle is observed as [TiO2@Silica] is increased above a threshold, being stronger in ethanol suspensions. Moreover, above this [TiO2@Silica] threshold, the optical transmittance of the ethanol suspension starts to be considerably lower than in water, despite scattering strength being higher in water. These results are attributed to localization of light induced by strong correlation in the scatterers' position as a consequence of the long-range Coulomb interaction between the TiO2@Silica nanoparticles. Light diffraction in TiO2@Silica suspensions (water and ethanol) shows strong correlation in the scatterers' position (structure seemingly cubic), being stronger in ethanol than in water (longer-range Coulomb interaction). As a result, we demonstrate in these colloidal suspensions for the first time, to our knowledge, strongly enhanced light-matter coupling through correlation-induced localization with klT much higher than unity and in an ordered colloidal-photonic structure. This strong enhancement of light-matter coupling by localization of light opens an avenue for manufacturing powerful sensing tools.

The Thermomechanical Properties of Thermally Evaporated Bismuth Triiodide Thin Films.

Bismuth triiodide (BiI3) has been studied in recent years with the aim of developing lead-free semiconductors for photovoltaics. It has also appeared in X-ray detectors due to the high density of the Bismuth element. This material is attractive as an active layer in solar cells, or may be feasible for conversion into perovskite-like material (MA3Bi2I9), being also suitable for photovoltaic applications. In this study, we report on the thermomechanical properties (stress, hardness, coefficient of thermal expansion, and biaxial and reduced Young's moduli) of BiI3 thin films deposited by thermal evaporation. The stress was determined as a function of temperature, adopting the thermally induced bending technique, which allowed us to extract the coefficient of thermal expansion (31 × 10-6 °C-1) and Young's biaxial modulus (19.6 GPa) for the films. Nanohardness (~0.76 GPa) and a reduced Young's modulus of 27.1 GPa were determined through nanoindentation measurements.

Molecular Electronics Including Temperature Effects Based on Dyes Pigments.

In this work we used the Density Functional Theory to study the thermodynamic properties from Brazilein (BZE) and Brazilin (BZI) molecules, main pigments responsible for the red color from Brazil wood. We did a comparison between the two dyes to then know which dye has better resistance to temperature (T ) and external electric field (E) values, aiming their potential to possible applications in solar cells, as excitons trainers. We have found that the BZE molecule becomes less stable after a temperature known as degradation temperature, and therefore enters oxidation state. However, BZE is more stable and more resistant to high temperatures. With respect to the applied external electric field, we find that BZE is more reactive to almost all the applied electric fields, thus more easily converted into energy in the form of electrical work.

Time-Dependent Density Functional Theory Analysis of Triphenylamine-Functionalized Graphene Doped with Transition Metals for Photocatalytic Hydrogen Production.

The electronic structures and optical properties of triphenylamine-functionalized graphene (G-TPA) doped with transition metals, using water as a solvent, were theoretically investigated to verify the efficiency of photocatalytic hydrogen production with the use of transition metals. This study was performed by Density Functional Theory and Time-dependent Density Functional Theory through Gaussian 09W software, adopting the B3LYP functional for all structures. The 6-31g(d) basis set was used for H, C and N atoms, and the LANL2DZ basis set for transition metals using the Effective Core Potentials method. Two approaches were adopted: (1) using single metallic dopants (Ni, Pd, Fe, Os and Pt) and (2) using combinations of Ni with the other dopants (NiPd, NiPt, NiFe and NiOs). The DOS spectra reveal an increase of accessible states in the valence shell, in addition to a gap decrease for all dopants. This doping also increases the absorption in the visible region of solar radiation where sunlight is most intense (400 nm to 700 nm), with additional absorption peaks. The results lead us to propose the G-TPA structures doped with Ni, Pd, Pt, NiPt or NiPd to be novel catalysts for the conversion of solar energy for photocatalytic hydrogen production, since they improve the absorption of solar energy in the range of interest for solar radiation; and act as reaction centers, reducing the required overpotential for hydrogen production from water.

Thermodynamic DFT analysis of natural gas.

Density functional theory was performed for thermodynamic predictions on natural gas, whose B3LYP/6-311++G(d,p), B3LYP/6-31+G(d), CBS-QB3, G3, and G4 methods were applied. Additionally, we carried out thermodynamic predictions using G3/G4 averaged. The calculations were performed for each major component of seven kinds of natural gas and to their respective air + natural gas mixtures at a thermal equilibrium between room temperature and the initial temperature of a combustion chamber during the injection stage. The following thermodynamic properties were obtained: internal energy, enthalpy, Gibbs free energy and entropy, which enabled us to investigate the thermal resistance of fuels. Also, we estimated an important parameter, namely, the specific heat ratio of each natural gas; this allowed us to compare the results with the empirical functions of these parameters, where the B3LYP/6-311++G(d,p) and G3/G4 methods showed better agreements. In addition, relevant information on the thermal and mechanic resistance of natural gases were investigated, as well as the standard thermodynamic properties for the combustion of natural gas. Thus, we show that density functional theory can be useful for predicting the thermodynamic properties of natural gas, enabling the production of more efficient compositions for the investigated fuels. Graphical abstract Investigation of the thermodynamic properties of natural gas through the canonical ensemble model and the density functional theory.