Deep learning strategy for small dataset from atomic force microscopy mechano-imaging on macrophages phenotypes.

The cytoskeleton is involved during movement, shaping, resilience, and functionality in immune system cells. Biomarkers such as elasticity and adhesion can be promising alternatives to detect the status of cells upon phenotype activation in correlation with functionality. For instance, professional immune cells such as macrophages undergo phenotype functional polarization, and their biomechanical behaviors can be used as indicators for early diagnostics. For this purpose, combining the biomechanical sensitivity of atomic force microscopy (AFM) with the automation and performance of a deep neural network (DNN) is a promising strategy to distinguish and classify different activation states. To resolve the issue of small datasets in AFM-typical experiments, nanomechanical maps were divided into pixels with additional localization data. On such an enlarged dataset, a DNN was trained by multimodal fusion, and the prediction was obtained by voting classification. Without using conventional biomarkers, our algorithm demonstrated high performance in predicting the phenotype of macrophages. Moreover, permutation feature importance was employed to interpret the results and unveil the importance of different biophysical properties and, in turn, correlated this with the local density of the cytoskeleton. While our results were demonstrated on the RAW264.7 model cell line, we expect that our methodology could be opportunely customized and applied to distinguish different cell systems and correlate feature importance with biophysical properties to unveil innovative markers for diagnostics.

Li2ZrO3-Coated Monocrystalline LiAl0.06Mn1.94O4 Particles as Cathode Materials for Lithium-Ion Batteries.

Li2ZrO3-coated and Al-doped micro-sized monocrystalline LiMn2O4 powder is synthesized through solid-state reaction, and the electrochemical performance is investigated as cathode materials for lithium-ion batteries. It is found that Li2ZrO3-coated LiAl0.06Mn1.94O4 delivers a discharge capacity of 110.90 mAhg-1 with 94% capacity retention after 200 cycles at room temperature and a discharge capacity of 104.4 mAhg-1 with a capacity retention of 87.8% after 100 cycles at 55 °C. Moreover, Li2ZrO3-coated LiAl0.06Mn1.94O4 could retain 87.5% of its initial capacity at 5C rate. This superior cycling and rate performance can be greatly contributed to the synergistic effect of Al-doping and Li2ZrO3-coating.

Effect of Nano-Y2O3 Content on Microstructure and Mechanical Properties of Fe18Cr Films Fabricated by RF Magnetron Sputtering.

In this work, FeCr-based films with different Y2O3 contents were fabricated using radio frequency (RF) magnetron sputtering. The effects of Y2O3 content on their microstructure and mechanical properties were investigated through scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS), inductive coupled plasma emission spectrometer (ICP) and a nanoindenter. It was found that the Y2O3-doped FeCr films exhibited a nanocomposite structure with nanosized Y2O3 particles uniformly distributed into a FeCr matrix. With the increase of Y2O3 content from 0 to 1.97 wt.%, the average grain size of the FeCr films decreased from 12.65 nm to 7.34 nm, demonstrating a grain refining effect of Y2O3. Furthermore, the hardness of the Y2O3-doped FeCr films showed an increasing trend with Y2O3 concentration, owing to the synergetic effect of dispersion strengthening and grain refinement strengthening. This work provides a beneficial guidance on the development and research of composite materials of nanocrystalline metal with a rare earth oxide dispersion phase.