A method for the fabrication of thick films of porous anodic alumina on rigid substrates is described. The anodic alumina film was generated by the anodization of an aluminum film evaporated on the substrate. The morphology of the barrier layer between the porous film and the substrate was different from that of anodic films grown on aluminum substrates. The removal of the barrier layer and the electrochemical growth of nanowires within the ordered pores were accomplished without the need to remove the anodic film from the substrate. We fabricated porous anodic alumina samples over large areas (up to 70 cm2), and deposited in them nanowire arrays of various materials. Long nanowires were obtained with lengths of at least 9 μm and aspect ratios as high as 300. Due to their mechanical robustness and the built‐in contact between the conducting substrate and the nanowires, the structures were useful for electrical transport measurements on the arrays. The method was also demonstrated on patterned and non‐planar substrates, further expanding the range of applications of these porous alumina and nanowire assemblies.

Electrochemistry offers opportunities to promote single-electron transfer (SET) redox-neutral chemistries similar to those recently discovered using visible-light photocatalysis but without the use of an expensive photocatalyst. Herein, we introduce a microfluidic redox-neutral electrochemistry (μRN-eChem) platform that has broad applicability to SET chemistry, including radical-radical cross-coupling, Minisci-type reactions, and nickel-catalyzed C(sp2)–O cross-coupling. The cathode and anode simultaneously generate the corresponding reactive intermediates, and selective transformation is facilitated by the rapid molecular diffusion across a microfluidic channel that outpaces the decomposition of the intermediates. μRN-eChem was shown to enable a two-step gram-scale electrosynthesis of a nematic liquid crystal compound, demonstrating its practicality.

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We present a machine learning-based method for tomographic reconstruction of dense layered objects, with range of projection angles limited to ±10○. Whereas previous approaches to phase tomography generally require 2 steps, first to retrieve phase projections from intensity projections and then to perform tomographic reconstruction on the retrieved phase projections, in our work a physics-informed preprocessor followed by a deep neural network (DNN) conduct the 3-dimensional reconstruction directly from the intensity projections. We demonstrate this single-step method experimentally in the visible optical domain on a scaled-up integrated circuit phantom. We show that even under conditions of highly attenuated photon fluxes a DNN trained only on synthetic data can be used to successfully reconstruct physical samples disjoint from the synthetic training set. Thus, the need for producing a large number of physical examples for training is ameliorated. The method is generally applicable to tomography with electromagnetic or other types of radiation at all bands.

Charged interface states are introduced by UV-ozone treatment of a polymer gate dielectric, parylene, prior to deposition of the organic semiconductor, pentacene, thereby modifying the organic field effect transistor (OFET) operation from enhancement to depletion mode. Quasistatic capacitance-voltage measurements and the corresponding current-voltage characteristics show that the threshold voltage VT and flatband voltage VFB can be shifted by over +50V, depending on the ozone exposure time. This work demonstrates that careful control of the semiconductor-insulator interface state densities is essential to VT and VFB control and the fabrication of reliable OFET integrated circuits.