Events and reaction mechanisms during the synthesis of an AI2O3-TiB2 nanocomposite via high energy ball milling

An AI2O3-TiB2 nanocomposite was success- fully synthesized by the high energy ball milling of A1, B2O3 and TiO2. The structures of the powdered particles formed at different milling times were evaluated by X-ray diffraction （XRD）, scanning electron microscopy （SEM） and transmission electron microscopy （TEM）. Thermo- dynamic calculations showed that the composite formed in two steps via highly exothermic mechanically induced self-sustaining reactions （MSRs）. The composite started to form at milling times of 9-10 h but the reaction was not complete. The remaining starting materials were consumed by increasing the milling time to 15 h. The XRD patterns of the annealed powders showed that aluminum borate is one of the intermediate products and that it is consumed at higher temperatures. Heat treatment of the 6-h milled sample at l l00℃ led to a complete formation of the composite. Increasing the milling time to 15 h led to a refining of the crystallite sizes. A nanocomposite powder with a mean crystallite size of 35-40 nm was obtained after milling for 15 h.

Machine learning metrology of cell confinement in melt electrowritten threedimensional biomaterial substrates

Tuning cell shape by altering the biophysical properties of biomaterial substrates on which cells operate would provide a potential shape-driven pathway to control cell phenotype.However,there is an unexplored dimensional scale window of three-dimensional(3D)substrates with precisely tunable porous microarchitectures and geometrical feature sizes at the cell’s operating length scales(10–100μm).This paper demonstrates the fabrication of such highfidelity fibrous substrates using a melt electrowriting(MEW)technique.This advanced manufacturing approach is biologically qualified with a metrology framework that models and classifies cell confinement states under various substrate dimensionalities and architectures.Using fibroblasts as a model cell system,the mechanosensing response of adherent cells is investigated as a function of variable substrate dimensionality(2D vs.3D)and porous microarchitecture(randomly oriented,“non-woven”vs.precision-stacked,“woven”).Single-cell confinement states are modeled using confocal fluorescence microscopy in conjunction with an automated single-cell bioimage data analysis workflow that extracts quantitative metrics of the whole cell and sub-cellular focal adhesion protein features measured.The extracted multidimensional dataset is employed to train a machine learning algorithm to classify cell shape phenotypes.The results show that cells assume distinct confinement states that are enforced by the prescribed substrate dimensionalities and porous microarchitectures with the woven MEW substrates promoting the highest cell shape homogeneity compared to non-woven fibrous substrates.The technology platform established here constitutes a significant step towards the development of integrated additive manufacturing—metrology platforms for a wide range of applications including fundamental mechanobiology studies and 3D bioprinting of tissue constructs to yield specific biological designs qualified at the single-cell level.