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Abstract Thinning silicon wafers via wet etching is a common practice in the microelectromechanical system (MEMS) industry to produce membranes and other structures Wang (Nano Lett 13(9): 4393–4398, 2013). Controlling the thickness of a membrane is a critical aspect to optimize the functionality of these devices. Our research specifically focuses on the production of bio-membranes for lung-on-a-chip (LoaC) applications. In our fabrication, it is crucial for us to determine the membranes’ thickness in a non-invasive way. This study aims to address this issue by attempting to develop a tool that uses the optical properties of light transmission through silicon to find a correlation with thickness. To find this correlation, we conducted a small experimental study where we fabricated ultra-thin membranes and captured images of the light transmission through these samples. This paper will report the correlation found between calculated average intensities of these images and measurements done using scanning electron microscopy (SEM). Graphical abstract 

Abstract In the last decade, organ-on-a-chip technology has been researched as an alternative to animal and cell culture models (Buhidma et al. in NPJ Parkinson’s Dis, 2020; Pearce et al. in Eur Cells Mater 13:1–10, 2007; Huh et al. in Nat Protoc 8:2135–2157, 2013). While extensive research has focused on the biological functions of these chips, there has been limited exploration of functional materials that can accurately replicate the biological environment. Our group concentrated on a lung-on-a-chip featuring a newly fabricated porous silicon bio-membrane. This bio-membrane mimics the interstitial space found between epithelial and endothelial cells in vivo, with a thickness of approximately 1 μm (Ingber in Cell 164:1105–1109, 2016). This study aims to establish a fabrication method for producing a thin, uniform porous silicon membrane with a predictablereduced modulus. We conducted mechanical and morphological characterization using scanning electron microscopy and nanoindentation. A small, parametric study was conducted to determine the reduced modulus of the porous silicon and how it may relate to the morphological features of the membrane. We compare our results to other works. Graphical Abstract 

Raman spectroscopy is a common identification and analysis technique used in research and manufacturing industries. This study investigates the use of Raman spectroscopy and deep learning techniques for identifying various nanofabrication chemicals. Four solvents and SU-8 developer were identified inside common chemical storage and distribution containers. The containers attenuated the spectra and contributed varying amounts of background fluorescence, making manual identification difficult. Two varieties of SU-8 photoresist were differentiated inside amber glass jars, and cured samples of three ratios of polydimethylsiloxane (PDMS) were differentiated using Raman microscopy. The neural network accurately identified the nanofabrication chemicals 100% of the time, without additional preprocessing. This investigation demonstrates the use of Raman spectroscopy and neural networks for the identification of nanofabrication chemicals and makes recommendations for use in other challenging identification applications. 

Bacteria identification can be a time-consuming process. Machine learning algorithms that use deep convolutional neural networks (CNNs) provide a promising alternative. Here, we present a deep learning based approach paired with Raman spectroscopy to rapidly and accurately detect the identity of a bacteria class. We propose a simple 4-layer CNN architecture and use a 30-class bacteria isolate dataset for training and testing. We achieve an identification accuracy of around 86% with identification speeds close to real-time. This optical/biological detection method is promising for applications in the detection of microbes in liquid biopsies and concentrated environmental liquid samples, where fast and accurate detection is crucial. This study uses a recently published dataset of Raman spectra from bacteria samples and an improved CNN model built with TensorFlow. Results show improved identification accuracy and reduced network complexity.