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Materiobiology is an emerging field focused on the physiochemical properties of biomaterials concerning biological outcomes which includes but is not limited to the biological responses and bioactivity of surface-modified biomaterials. 

Abstract Hydrologic modeling has been a useful approach for analyzing water partitioning in catchment systems. It will play an essential role in studying the responses of watersheds under projected climate changes. Numerous studies have shown it is critical to include subsurface heterogeneity in the hydrologic modeling to correctly simulate various water fluxes and processes in the hydrologic system. In this study, we test the idea of incorporating geophysics‐obtained subsurface critical zone (CZ) structures in the hydrologic modeling of a mountainous headwater catchment. The CZ structure is extracted from a three‐dimensional seismic velocity model developed from a series of two‐dimensional velocity sections inverted from seismic travel time measurements. Comparing different subsurface models shows that geophysics‐informed hydrologic modeling better fits the field observations, including streamflow discharge and soil moisture measurements. The results also show that this new hydrologic modeling approach could quantify many key hydrologic fluxes in the catchment, including streamflow, deep infiltration, and subsurface water storage. Estimations of these fluxes from numerical simulations generally have low uncertainties and are consistent with estimations from other methods. In particular, it is straightforward to calculate many hydraulic fluxes or states that may not be measured directly in the field or separated from field observations. Examples include quickflow/subsurface lateral flow, soil/rock moisture, and deep infiltration. Thus, this study provides a useful approach for studying the hydraulic fluxes and processes in the deep subsurface (e.g., weathered bedrock), which needs to be better represented in many earth system models. 

Osteoblastic and chemical responses to Poly (ether ether ketone) (PEEK) material have been improved using a variety of low-temperature plasmas (LTPs). Surface chemical properties are modified, and can be used, using low-temperature plasma (LTP) treatments which change surface functional groups. These functional groups increase biomineralization, in simulated body fluid conditions, and cellular viability. PEEK scaffolds were treated, with a variety of LTPs, incubated in simulated body fluids, and then analyzed using multiple techniques. First, scanning electron microscopy (SEM) showed morphological changes in the biomineralization for all samples. Calcein staining, Fourier transform infrared spectroscopy (FTIR), and X-ray photoelectron spectroscopy (XPS) confirmed that all low-temperature plasma-treated groups showed higher levels of biomineralization than the control group. MTT cell viability assays showed LTP-treated groups had increased cell viability in comparison to non-LTP-treated controls. PEEK treated with triethyl phosphate plasma (TEP) showed higher levels of cellular viability at 82.91% ± 5.00 (n = 6) and mineralization. These were significantly different to both the methyl methacrylate (MMA) 77.38% ± 1.27, ethylene diamine (EDA) 64.75% ± 6.43 plasma-treated PEEK groups, and the control, non-plasma-treated group 58.80 ± 2.84. FTIR showed higher levels of carbonate and phosphate formation on the TEP-treated PEEK than the other samples; however, calcein staining fluorescence of MMA and TEP-treated PEEK had the highest levels of biomineralization measured by pixel intensity quantification of 101.17 ± 4.63 and 96.35 ± 3.58, respectively, while EDA and control PEEK samples were 89.53 ± 1.74 and 90.49 ± 2.33, respectively. Comparing different LTPs, we showed that modified surface chemistry has quantitatively measurable effects that are favorable to the cellular, biomineralization, and chemical properties of PEEK. 

This is for the paper Geophysics-informed hydrologic modeling of a mountain headwater catchment for studying hydrological partitioning in the critical zone. Also check the description in https://github.com/geohang/Geophysics_informed_models 

We exploit the different but complementary data sensitivities of ground-penetrating radar (GPR) and electrical resistivity (ER) by applying a multiphysics, multiparameter, simultaneous 2.5D joint inversion without invoking petrophysical relationships. Our method joins full-waveform inversion (FWI) GPR with adjoint derived ER sensitivities on the same computational domain. We incorporate a stable source estimation routine into the FWI-GPR. We apply our method in a controlled alluvial aquifer using only surface-acquired data. The site exhibits a shallow groundwater boundary and unconsolidated heterogeneous alluvial deposits. We compare our recovered parameters to individual FWI-GPR and ER results, and we compare them to log measurements of capacitive conductivity and neutron-derived porosity. Our joint inversion provides a more representative depiction of subsurface structures because it incorporates multiple intrinsic parameters, and it is therefore superior to an interpretation based on log data, FWI-GPR, or ER alone. 

We have developed a memory and operation-count efficient 2.5D inversion algorithm of electrical resistivity (ER) data that can handle fine discretization domains imposed by other geophysical (e.g, ground penetrating radar or seismic) data. Due to numerical stability criteria and available computational memory, joint inversion of different types of geophysical data can impose different grid discretization constraints on the model parameters. Our algorithm enables the ER data sensitivities to be directly joined with other geophysical data without the need of interpolating or coarsening the discretization. We have used the adjoint method directly in the discretized Maxwell’s steady state equation to compute the data sensitivity to the conductivity. In doing so, we make no finite-difference approximation on the Jacobian of the data and avoid the need to store large and dense matrices. Rather, we exploit matrix-vector multiplication of sparse matrices and find successful convergence using gradient descent for our inversion routine without having to resort to the Hessian of the objective function. By assuming a 2.5D subsurface, we are able to linearly reduce memory requirements when compared to a 3D gradient descent inversion, and by a power of two when compared to storing a 2D Hessian. Moreover, our method linearly outperforms operation counts when compared with 3D Gauss-Newton conjugate-gradient schemes, which scales cubically in our favor with respect to the thickness of the 3D domain. We physically appraise the domain of the recovered conductivity using a cutoff of the electric current density present in our survey. We evaluate two case studies to assess the validity of our algorithm. First, on a 2.5D synthetic example, and then on field data acquired in a controlled alluvial aquifer, where we were able to match the recovered conductivity to borehole observations.