Electron transmission through suspended graphene membranes measured with a low-voltage gated Si field emitter array.

We experimentally demonstrate the transmission of electrons through different number (1, 2, and 5) of suspended graphene layers at electron energies between 20 and 250 eV. Electrons with initial energies lower than 40 eV are generated using silicon field emitter arrays with 1µm pitch, and accelerated towards the graphene layers supported by a silicon nitride grid biased at voltages from -20 to 200 V. We measured significant increase in current collected at the anode with the presence of graphene, which is attributed to the possible generation of secondary electrons by primary electrons impinging on the graphene membrane. Highest output current was recorded with monolayer graphene at approximately 90 eV, with up to 1.7 times the incident current. The transparency of graphene to low-energy electrons and its impermeability to gas molecules could enable low-voltage field emission electron sources, which often require ultra-high vacuum, to operate in a relatively poor vacuum environment.

Microfluidic electrochemistry for single-electron transfer redox-neutral reactions.

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.

Nanoscale silicon field emitter arrays with self-aligned extractor and focus gates.

Out-of-plane focusing is essential for electron beam collimation in gated field emission sources. The focus electrode redirects electrons emitted by the tip with a wide angle towards the central axis, resulting a small focal spot at the anode. Here, we demonstrate for the first time, very high density (108 emitters/cm2) arrays of double-gated field emission electron sources with self-aligned apertures and integrated nanowire current limiters. Release of the emitters after fabrication required the combination of a highly selective dry-etch and an isotropic wet-etch to avoid the loss of the insulator between the two gates. The aperture diameters are ∼360 nm and ∼570 nm for the extractor gate and focus gate, respectively. The turn-on voltage was low (15-20) V and anode currents of 400 nA were measured at 25 V. We compared devices with different extractor gate thicknesses resulting from planarization non-uniformity, and demonstrate the influence of the focus gate on anode current. The focal spot size was measured, using a low energy phosphor screen, to be around 700 µm for a 500 µm device when the [Formula: see text] ratio was 0.35.

High-resolution limited-angle phase tomography of dense layered objects using deep neural networks.

We present a machine learning-based method for tomographic reconstruction of dense layered objects, with range of projection angles limited to [Formula: see text] 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.