Magnetic resonance imaging of blood perfusion rate based on Helmholtz decomposition of heat flux.

Objective.Thermal property (TP) maps of human tissues are useful for tumor treatment and diagnosis. In particular, the blood perfusion rate is significantly different for tumors and healthy tissues. Noninvasive techniques that reconstruct TPs from the temperature measured via magnetic resonance imaging (MRI) by solving an inverse bioheat transfer problem have been developed. A few conventional methods can reconstruct spatially varying TP distributions, but they have several limitations. First, most methods require the numerical Laplacian computation of the temperature, and hence they are sensitive to noise. In addition, some methods require the division of a region of interest (ROI) into sub-regions with homogeneous TPs using prior anatomical information, and they assume an unmeasurable initial temperature distribution. We propose a novel robust reconstruction method without the division of an ROI or the assumption of an initial temperature distribution.Approach.The proposed method estimates blood perfusion rate maps from relative temperature changes. This method avoids the computation of the Laplacian by using integral representations of the Helmholtz decomposition of the heat flux.Main Result.We compare the reconstruction results of the conventional and proposed methods using numerical simulations. The results indicate the robustness of the proposed method.Significance.This study suggests the feasibility of thermal property mapping with MRI using the robust proposed method.

Review: Advanced Atomic Force Microscopy Modes for Biomedical Research.

Visualization of biomedical samples in their native environments at the microscopic scale is crucial for studying fundamental principles and discovering biomedical systems with complex interaction. The study of dynamic biological processes requires a microscope system with multiple modalities, high spatial/temporal resolution, large imaging ranges, versatile imaging environments and ideally in-situ manipulation capabilities. Recent development of new Atomic Force Microscopy (AFM) capabilities has made it such a powerful tool for biological and biomedical research. This review introduces novel AFM functionalities including high-speed imaging for dynamic process visualization, mechanobiology with force spectroscopy, molecular species characterization, and AFM nano-manipulation. These capabilities enable many new possibilities for novel scientific research and allow scientists to observe and explore processes at the nanoscale like never before. Selected application examples from recent studies are provided to demonstrate the effectiveness of these AFM techniques.

Microstructure, mechanical properties and elemental composition of the terrestrial isopod Armadillidium vulgare cuticle.

The exoskeletons of crustaceans are essential for providing protection from predators and other environmental threats. Understanding the structure and mechanical behavior of their natural armor could inspire the design of lightweight and high toughness synthetic materials. Most published work has focused on marine crustacea rather than their terrestrial counterparts, which are exposed to a multitude of unique threats. The interest in the terrestrial isopod Armadillidium vulgare (A. vulgare) has grown but the interrelationship between the microstructure, chemical composition, and mechanical properties has not been thoroughly investigated. Thus, this study aims to elucidate missing details concerning this biological mineralized composite. Exoskeleton specimens were fixated to preserve the intrinsic protein structure. We utilize scanning electron microscopy for microstructure analysis, Raman spectroscopy for elemental analysis, and nanoindentation property mapping to achieve mechanical characterization. The naturally fractured A. vulgare exoskeleton cross-section reveals four subregions with the repeating helicoidal 'Bouligand' arrangement most prominent in the endocuticle. The hardness and reduced modulus distributions exhibit a through-thickness exponential gradient with decreasing magnitudes from the outermost to the innermost layers of the exoskeleton. The Raman spectra show a graded spatial distribution of key constituents such as calcium carbonate across the thickness, some of which are consistent with the mechanical property gradient. Potential microstructure, elemental composition, and mechanical property relationships are discussed to explain how the hierarchical structure of this nanolaminate armor protects this species.

Optical Property Mapping of Apples and the Relationship With Quality Properties.

This paper reports on the measurement of optical property mapping of apples at the wavelengths of 460, 527, 630, and 710 nm using spatial-frequency domain imaging (SFDI) technique, for assessing the soluble solid content (SSC), firmness, and color parameters. A laboratory-based multispectral SFDI system was developed for acquiring SFDI of 140 "Golden Delicious" apples, from which absorption coefficient (µ a ) and reduced scattering coefficient (µ s ') mappings were quantitatively determined using the three-phase demodulation coupled with curve-fitting method. There was no noticeable spatial variation in the optical property mapping based on the resulting effect of different sizes of the region of interest (ROI) on the average optical properties. Support vector machine (SVM), multiple linear regression (MLR), and partial least square (PLS) models were developed based on µ a , µ s ' and their combinations (µ a × µ s ' and µ eff ) for predicting apple qualities, among which SVM outperformed the best. Better prediction results for quality parameters based on the µ a were observed than those based on the µ s ', and the combinations further improved the prediction performance, compared to the individual µ a or µ s '. The best prediction models for SSC and firmness parameters [slope, flesh firmness (FF), and maximum force (Max.F)] were achieved based on the µ a × µ s ', whereas those for color parameters of b* and C* were based on the µ eff , with the correlation coefficients of prediction as 0.66, 0.68, 0.73, 0.79, 0.86, and 0.86, respectively.

Advanced Nanomechanical Characterization of Biopolymer Films Containing GNPs and MWCNTs in Hybrid Composite Structure.

Nanomechanical definition of the properties of composite specimens based on polylactic acid (PLA) was made in the present study. Research activities with accent on biodegradable polymer nanocomposites have fundamental significance originated from the worldwide plastic waste pollution. To receive hybrid nanocomposites with high level of homogeneity, the low cost and environmentally friendly melt extrusion method has been applied. The role of graphene nanoplatelets (GNPs) and multiwall carbon nanotubes (MWCNTs) as reinforcing nanoparticles dispersed in the polymer matrix was thoroughly investigated. Quasi-static nanoindentation analysis was enriched by performance of accelerated property mapping and nanodynamic mechanical testing in order to fully describe the nanoscale surface homogeneity and stress relaxation behavior of the nanocomposite specimens. That novelty of the research approach had a well-marked contribution over the detection of the new samples' nanomechanical features as a function of the type of carbon nanofiller. Refined nanoscratch experiments uncovered the resistance of the materials against notches by means of measurement of the coefficient of friction and accurate estimation of the residual penetration depth.

Structural Response Prediction of Thin-Walled Additively Manufactured Parts Considering Orthotropy, Thickness Dependency and Scatter.

Besides the design freedom offered by additive manufacturing, another asset lies within its potential to accelerate product development processes by rapid fabrication of functional prototypes. The premise to fully exploit this benefit for lightweight design is the accurate structural response prediction prior to part production. However, the peculiar material behavior, characterized by anisotropy, thickness dependency and scatter, still constitutes a major challenge. Hence, a modeling approach for finite element analysis that accounts for this inhomogeneous behavior is developed by example of laser-sintered short-fiber-reinforced polyamide 12. Orthotropic and thickness-dependent Young's moduli and Poisson's ratios were determined via quasi-static tensile tests. Thereof, material models were generated and implemented in a property mapping routine for finite element models. Additionally, a framework for stochastic finite element analysis was set up for the consideration of scatter in material properties. For validation, thin-walled parts on sub-component level were fabricated and tested in quasi-static three-point bending experiments. Elastic parameters showed considerable anisotropy, thickness dependency and scatter. A comparison of the predicted forces with experimentally evaluated reaction forces disclosed substantially improved accuracy when utilizing the novel inhomogeneous approach instead of conventional homogeneous approaches. Furthermore, the variability observed in the structural response of loaded parts could be reproduced by the stochastic simulations.

Combinatorial Exploration and Mapping of Phase Transformation in a Ni-Ti-Co Thin Film Library.

Combinatorial synthesis and high-throughput characterization of a Ni-Ti-Co thin film materials library are reported for exploration of reversible martensitic transformation. The library was prepared by magnetron co-sputtering, annealed in vacuum at 500 °C without atmospheric exposure, and evaluated for shape memory behavior as an indicator of transformation. Composition, structure, and transformation behavior of the 177 pads in the library were characterized using high-throughput wavelength dispersive spectroscopy (WDS), X-ray photoelectron spectroscopy (XPS), X-ray diffraction (XRD), and four-point probe temperature-dependent resistance (R(T)) measurements. A new, expanded composition space having phase transformation with low thermal hysteresis and Co > 10 at. % is found. Unsupervised machine learning methods of hierarchical clustering were employed to streamline data processing of the large XRD and XPS data sets. Through cluster analysis of XRD data, we identified and mapped the constituent structural phases. Composition-structure-property maps for the ternary system are made to correlate the functional properties to the local microstructure and composition of the Ni-Ti-Co thin film library.

COMBIgor: Data-Analysis Package for Combinatorial Materials Science.

Combinatorial experiments involve synthesis of sample libraries with lateral composition gradients requiring spatially resolved characterization of structure and properties. Because of the maturation of combinatorial methods and their successful application in many fields, the modern combinatorial laboratory produces diverse and complex data sets requiring advanced analysis and visualization techniques. In order to utilize these large data sets to uncover new knowledge, the combinatorial scientist must engage in data science. For data science tasks, most laboratories adopt common-purpose data management and visualization software. However, processing and cross-correlating data from various measurement tools is no small task for such generic programs. Here we describe COMBIgor, a purpose-built open-source software package written in the commercial Igor Pro environment and designed to offer a systematic approach to loading, storing, processing, and visualizing combinatorial data. It includes (1) methods for loading and storing data sets from combinatorial libraries, (2) routines for streamlined data processing, and (3) data-analysis and -visualization features to construct figures. Most importantly, COMBIgor is designed to be easily customized by a laboratory, group, or individual in order to integrate additional instruments and data-processing algorithms. Utilizing the capabilities of COMBIgor can significantly reduce the burden of data management on the combinatorial scientist.

The atypically high modulus of pollen exine.

Sporopollenin, the polymer comprising the exine (outer solid shell) of pollen, is recognized as one of the most chemically and mechanically stable naturally occurring organic substances. The elastic modulus of sporopollenin is of great importance to understanding the adhesion, transport and protective functions of pollen grains. In addition, this fundamental mechanical property is of significant interest in using pollen exine as a material for drug delivery, reinforcing fillers, sensors and adhesives. Yet, the literature reports of the elastic modulus of sporopollenin are very limited. We provide the first report of the elastic modulus of sporopollenin from direct indentation of pollen particles of three plant species: ragweed (Ambrosia artemisiifolia), pecan (Carya illinoinensis) and Kentucky bluegrass (Poa pratensis). The modulus was determined with atomic force microscopy by using direct nanomechanical mapping of the pollen shell surface. The moduli were atypically high for non-crystalline organic biomaterials, with average values of 16 ± 2.5 GPa (ragweed), 9.5 ± 2.3 GPa (pecan) and 16 ± 4.0 GPa (Kentucky bluegrass). The amorphous pollen exine has a modulus exceeding known non-crystalline biomaterials, such as lignin (6.7 GPa) and actin (1.8 GPa). In addition to native pollen, we have investigated the effects of exposure to a common preparative base-acid chemical treatment and elevated humidity on the modulus. Base-acid treatment reduced the ragweed modulus by up to 58% and water vapour exposure at 90% relative humidity reduced the modulus by 54% (pecan) and 72% (Kentucky bluegrass). These results are in agreement with recently published estimates of the modulus of base-acid-treated ragweed pollen of 8 GPa from fitting to mechanical properties of ragweed pollen-epoxy composites.

Nano-palpation AFM and its quantitative mechanical property mapping.

We review nano-palpation atomic force microscopy, which offers quantitative mechanical property mapping especially for soft materials. The method measures force-deformation curves on the surfaces of soft materials. The emphasis is placed on how both Hertzian and Derjaguin-Muller-Toporov contact mechanics fail to reproduce the experimental curves and, alternatively, how the Johnson-Kendall-Roberts model does. We also describe the force-volume technique for obtaining a two-dimensional map of mechanical properties, such as the elastic modulus and adhesive energy, based on the above-mentioned analysis. Finally, we conclude with several counterpart measurements, which describe the viscoelastic nature of soft materials, and give examples, including vulcanized isoprene rubber and the current status of ISO standardization.