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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).

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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.

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Methods to probe and understand the dynamic response of materials following impulsive excitation are important for many fields, from materials and energy sciences to chemical and neuroscience. To design more efficient nano, energy, and quantum devices, new methods are needed to uncover the dominant excitations and reaction pathways. In this work, we implement a newly-developed superlet transform—a super-resolution time-frequency analytical method—to analyze and extract phonon dynamics in a laser-excited two-dimensional (2D) quantum material. This quasi-2D system, 1T-TaSe2, supports both equilibrium and metastable light-induced charge density wave (CDW) phases mediated by strongly coupled phonons. We compare the effectiveness of the superlet transform to standard time-frequency techniques. We find that the superlet transform is superior in both time and frequency resolution, and use it to observe and validate novel physics. In particular, we show fluence-dependent changes in the coupled dynamics of three phonon modes that are similar in frequency, including the CDW amplitude mode, that clearly demonstrate a change in the dominant charge-phonon couplings. More interestingly, the frequencies of the three phonon modes, including the strongly-coupled CDW amplitude mode, remain time- and fluence-independent, which is unusual compared to previously investigated materials. Our study opens a new avenue for capturing the coherent evolution and couplings of strongly-coupled materials and quantum systems. 

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. 

The use of conventional in vitro and preclinical animal models often fail to properly recapitulate the complex nature of human diseases and hamper the success of translational therapies in humans [1-3] Consequently, research has moved towards organ-on-chip technology to better mimic human tissue interfaces and organ functionality. Herein, we describe a novel approach for the fabrication of a biocompatible membrane made of porous silicon (PSi) for use in organ-on-chip technology that provides key advantages when modeling complex tissue interfaces seen in vivo. By combining well-established methods in the semiconductor industry with organ-on-chip technology, we have developed a novel way of producing thin (25 μm) freestanding PSi biocompatible membranes with both nano (~15.5 nm diameter pores) and macroporous (~0.5 μm diameter pores) structures. To validate the proposed novel membrane, we chose to recapitulate the dynamic environment of the alveolar blood gas exchange interface in alveolar co-culture. Viability assays and immunofluorescence imaging indicate that human pulmonary cells remain viable on the PSi membrane during long-term culture (14 days). Interestingly, it was observed that macrophages can significantly remodel and degrade the PSi membrane substrate in culture. This degradation will allow for more intimate physiological cellular contact between cells, mimicking a true blood-gas exchange interface as observed in vivo. Broadly, we believe that this novel PSi membrane may be used in more complex organ-on-chip and lab-on-chip model systems to accurately recapitulate human anatomy and physiology to provide further insight into human disease pathology and pre-clinical response to therapeutics.

 

Measuring and analyzing local field potential (LFP) signals from basolateral amygdala (BLA), hippocampus (HPC) and medial prefrontal cortex (mPFC) may help understand how they communicate with each other during fear memory formation and extinction. In our research, we have formulated a computationally simple and noise immune instantaneous amplitude cross correlation technique which can deduce lead and lag of LFPs generated in BLA, HPC, and mPFC and the directionality of brain signals exchanged between regions. LFP signals are recorded using depth electrodes in the rat brain and cross correlation analysis is applied to theta wave signals after filtering. We found that rats resilient to traumatic conditions (based on post-stress rapid eye movement sleep (REM)) showed a decrease in LFP signal correlation in REM and non-REM (NREM) sleep cycles between BLA-HPC regions after shock training and one day post shock training compared to vulnerable rats that show stress-induced reductions in REM. It is presumed this difference in neural network behavior may be related to REM sleep differences between resilient and vulnerable rats and may provide clues to help understand how traumatic conditions are processed by the brain. 

Electrical impedance tomography (EIT) is a rising and emerging imaging technique with great potential in many areas, especially in functional brain imaging applications. An EIT system with high speed and accuracy can have many applications to medical devices supporting in diagnosis and treatment of neurological disorders and diseases. In this research, EIT algorithms and hardware are developed and improved to increase reconstructed images' accuracy and decrease the reconstruction time. Due to multiplexer design limitations, EIT measurements are subject to strong capacitive effects from charging and discharging in switching cycles around 300 to 400 samples per 1280 samples (in 10 milliseconds sampling). We developed an algorithm to choose data in steady-state condition only selectively. This method improves the signal-to-noise ratio and results in better reconstruction images. An algorithm to effectively synchronize the beginning points of data was developed to increase the system's speed. This presentation also presents the EIT system's hardware architecture based on Texas Instruments Fixed-Point Digital Signal Processor - TMS320VC5509A, which is low-cost, high potential in popularity the community in the future. For high operation speed, we propose the EIT system used Sitara™ AM57x processors of Texas Instruments. 

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. 

Surface plasmon resonance is widely studied and used for chemical and biological sensing. Current technology is based on angle resolved resonance detection at specific optical wavelengths. That is, changes in the reflectivity at the resonant angles are correlated to the chemical or biological substance at the surface of the sensor. In this work, we discuss the modeling and numerical techniques used to analyze a method to characterize plasmon resonances through surface acoustic wave (SAW) coupling of the incident light. The design strategies used to optimize the sensing performance of layered structures is described for several materials that are typically used as substrates and thin films.