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The integration of gallium nitride (GaN) nanowire light-emitting diodes (nanoLEDs) on flexible substrates offers opportunities for applications beyond rigid solid-state lighting (e.g., for wearable optoelectronics and bendable inorganic displays). Here, we report on a fast physical transfer route based on femtosecond laser lift-off (fs-LLO) to realize wafer-scale top–down GaN nanoLED arrays on unconventional platforms. Combined with photolithography and hybrid etching processes, we successfully transferred GaN blue nanoLEDs from a full two-inch sapphire substrate onto a flexible copper (Cu) foil with a high nanowire density (~107 wires/cm2), transfer yield (~99.5%), and reproducibility. Various nanoanalytical measurements were conducted to evaluate the performance and limitations of the fs-LLO technique as well as to gain insights into physical material properties such as strain relaxation and assess the maturity of the transfer process. This work could enable the easy recycling of native growth substrates and inspire the development of large-scale hybrid GaN nanowire optoelectronic devices by solely employing standard epitaxial LED wafers (i.e., customized LED wafers with additional embedded sacrificial materials and a complicated growth process are not required).

Performing long-term cell observations is a non-trivial task for conventional optical microscopy, since it is usually not compatible with environments of an incubator and its temperature and humidity requirements. Lensless holographic microscopy, being entirely based on semiconductor chips without lenses and without any moving parts, has proven to be a very interesting alternative to conventional microscopy. Here, we report on the integration of a computational parfocal feature, which operates based on wave propagation distribution analysis, to perform a fast autofocusing process. This unique non-mechanical focusing approach was implemented to keep the imaged object staying in-focus during continuous long-term and real-time recordings. A light-emitting diode (LED) combined with pinhole setup was used to realize a point light source, leading to a resolution down to 2.76 μm. Our approach delivers not only in-focus sharp images of dynamic cells, but also three-dimensional (3D) information on their (x, y, z)-positions. System reliability tests were conducted inside a sealed incubator to monitor cultures of three different biological living cells (i.e., MIN6, neuroblastoma (SH-SY5Y), and Prorocentrum minimum). Altogether, this autofocusing framework enables new opportunities for highly integrated microscopic imaging and dynamic tracking of moving objects in harsh environments with large sample areas.

This paper reports on the fabrication of 3D silicon nanopillars (NPLs) modified with ZnO nanorods (NRs) (i.e., ZnO-NRs@Si-NPLs) on a piezoresistive Si microcantilever (MC) for sensing application. A monolayer-colloidal-crystal film of polystyrene (PS) nanoparticles (NPs) was deposited and used as physical mask for cryogenic Si dry etching. Vertically aligned Si-NPLs were selectively etched on the MC backside-surface. In addition, ZnO-NRs were grown in-situ on the Si NPLs using a sputtering/oxidizing and chemical bath deposition two-step method, resulting in a 3D ZnO-NRs@Si-NPLs on the MC backside.

Cell registration by artificial neural networks (ANNs) in combination with principal component analysis (PCA) has been demonstrated for cell images acquired by light emitting diode (LED)-based compact holographic microscopy. In this approach, principal component analysis was used to find the feature values from cells and background, which would be subsequently employed as neural inputs into the artificial neural networks. Image datasets were acquired from multiple cell cultures using a lensless microscope, where the reference data was generated by a manually analyzed recording. To evaluate the developed automatic cell counter, the trained system was assessed on different data sets to detect immortalized mouse astrocytes, exhibiting a detection accuracy of ~81% compared with manual analysis. The results show that the feature values from principal component analysis and feature learning by artificial neural networks are able to provide an automatic approach on the cell detection and registration in lensless holographic imaging.

Pinhole‐shaped light‐emitting diode (LED) arrays with dimension ranging from 100 μm down to 5 μm have been developed as point illumination sources. The proposed microLED arrays, which are based on gallium nitride (GaN) technology and emitting in the blue spectral region (λ = 465 nm), are integrated into a compact lensless holographic microscope for a non‐invasive, label‐free cell sensing and imaging. From the experimental results using single pinhole LEDs having a diameter of 90 μm, the reconstructed images display better resolution and enhanced image quality compared to those captured using a commercial surface‐mount device (SMD)‐based LED.

A compact lensless microscope comprising a custom-made LED engine and a CMOS imaging sensor has been developed for live-cell culture imaging inside a cell incubator environment. The imaging technique is based on digital inline-holographic microscopy, while the image reconstruction is carried out by angular spectrum approach with a custom written software. The system was tested with various biological samples including immortalized mouse astrocyte cells inside a petri dish. Besides the imaging possibility, the capability of automated cell counting and tracking could be demonstrated. By using image sensors capable of video frame rate, time series of cell movement can be captured.