Publications by authors named "Lesley Chan"

Nanoscale light emitting diodes (nanoLEDs, diameter < 1 µm), with active and sacrificial multi-quantum well (MQW) layers epitaxially grown via metal organic chemical vapor deposition, were fabricated and released into solution using a combination of colloidal lithography and photoelectrochemical (PEC) etching of the sacrificial MQW layer. PEC etch conditions were optimized to minimize undercut roughness, and thus limit damage to the active MQW layer. NanoLED emission was blue-shifted ∼10 nm from as-grown (unpatterned) LED material, hinting at strain relaxation in the active InGaN MQW layer.

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Strain in InGaN/GaN multiple-quantum well (MQW) light emitters was relaxed via nanopatterning using colloidal lithography and top-down plasma etching. Colloidal lithography was performed using Langmuir-Blodgett dip-coating of samples with silica particles (d = 170, 310, 690, 960 nm) and a Cl/N inductively coupled plasma etch to produce nanorod structures. The InGaN/GaN MQW nanorods were characterized using X-ray diffraction (XRD) reciprocal space mapping to quantify the degree of relaxation.

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Many mood tracking apps are available on smartphone app stores, but little is known about their features and their users' experiences. To investigate commercially available mood tracking apps, we conducted an in-depth feature analysis of 32 apps, and performed a qualitative analysis of a set of user reviews. Informed by a widely adopted personal informatics framework, we conducted a feature analysis to investigate how these apps support four stages of selftracking: preparation, collection, reflection, and action; and found that mood tracking apps offer many features for the collection and reflection stages, but lack adequate support for the preparation and action stages.

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Near- and sub-wavelength photonic structures are used by numerous organisms (e.g. insects, cephalopods, fish, birds) to create vivid and often dynamically-tunable colors, as well as create, manipulate, or capture light for vision, communication, crypsis, photosynthesis, and defense.

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Light extraction from InGaN/GaN-based multiple-quantum-well (MQW) light emitters is enhanced using a simple, scalable, and reproducible method to create hexagonally close-packed conical nano- and micro-scale features on the backside outcoupling surface. Colloidal lithography via Langmuir-Blodgett dip-coating using silica masks (d = 170-2530 nm) and Cl/N-based plasma etching produced features with aspect ratios of 3:1 on devices grown on semipolar GaN substrates. InGaN/GaN MQW structures were optically pumped at 266 nm and light extraction enhancement was quantified using angle-resolved photoluminescence.

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Nanostructured forms of crystalline silicon represent an attractive materials building block for photovoltaics due to their potential benefits to significantly reduce the consumption of active materials, relax the requirement of materials purity for high performance, and hence achieve greatly improved levelized cost of energy. Despite successful demonstrations for their concepts over the past decade, however, the practical application of nanostructured silicon solar cells for large-scale implementation has been hampered by many existing challenges associated with the consumption of the entire wafer or expensive source materials, difficulties to precisely control materials properties and doping characteristics, or restrictions on substrate materials and scalability. Here we present a highly integrable materials platform of nanostructured silicon solar cells that can overcome these limitations.

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Human pluripotent stem cells (hPSCs) are a promising cell source for tissue engineering and regenerative medicine, especially in the field of neurobiology. Neural differentiation protocols have been developed to differentiate hPSCs into specific neural cells, but these predominantly rely on biochemical cues. Recently, differentiation protocols have incorporated topographical cues to increase the total neuronal yield.

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Efficient derivation of neural cells from human embryonic stem cells (hESCs) remains an unmet need for the treatment of neurological disorders. The limiting factors for current methods include being labor-intensive, time-consuming and expensive. In this study, we hypothesize that the substrate topography, with optimal geometry and dimension, can modulate the neural fate of hESCs and enhance the efficiency of differentiation.

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Human embryonic stem cells (hESCs) are a promising cell source for tissue engineering and regenerative medicine, but before they can be used in therapies, we must be able to accurately identify the state and progeny of hESCs. One of the most commonly used methods for identification is flow cytometry. Many flow cytometry applications use antibodies to detect the amount of antigen present on/in a cell.

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The interplay of biophysical and biochemical cues in the extracellular microenvironment regulate and control the cell fate of stem cells. Understanding the interaction between stem cells and the extracellular substrate will be crucial in controlling stem cell differentiation for regenerative medicine applications. One of the biophysical properties of the microenvironment is substrate topology, which has been demonstrated to be an important mediator of stem cell lineage regulation.

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