Non-Invasive Blood Flow Speed Measurement Using Optics.

Non-invasive measurement of the arterial blood speed gives important health information such as cardio output and blood supplies to vital organs. The magnitude and change in arterial blood speed are key indicators of the health conditions and development and progression of diseases. We demonstrated a simple technique to directly measure the blood flow speed in main arteries based on the diffused light model. The concept is demonstrated with a phantom that uses intralipid hydrogel to model the biological tissue and an embedded glass tube with flowing human blood to model the blood vessel. The correlation function of the measured photocurrent was used to find the electrical field correlation function via the Siegert relation. We have shown that the characteristic decorrelation rate (i.e., the inverse of the decoherent time) is linearly proportional to the blood speed and independent of the tube diameter. This striking property can be explained by an approximate analytic solution for the diffused light equation in the regime where the convective flow is the dominating factor for decorrelation. As a result, we have demonstrated a non-invasive method of measuring arterial blood speed without any prior knowledge or assumption about the geometric or mechanic properties of the blood vessels.

Frequency- and Power-Dependent Photoresponse of a Perovskite Photodetector Down to the Single-Photon Level.

Organometallic halide perovskites attract strong interests for their high photoresponsivity and solar cell efficiency. However, there was no systematic study of their power- and frequency-dependent photoresponsivity. We identified two different power-dependent photoresponse types in methylammonium lead iodide perovskite (MAPbI3) photodetectors. In the first type, the photoresponse remains constant from 5 Hz to 800 MHz. In the second type, absorption of a single photon can generate a persistent photoconductivity of 30 pA under an applied electric field of 2.5 × 104 V/cm. Additional absorbed photons, up to 8, linearly increase the persistent photoconductivity, which saturates with the absorption of more than 10 photons. This is different than single-photon avalanche detectors (SPADs) because the single-photon response is persistent as long as the device is under bias, providing unique opportunities for novel electronic and photonic devices such as analogue memories for neuromorphic computing. We propose an avalanche-like process for iodine ions and estimate that absorption of a single 0.38 aJ photon triggers the motion of 108-9 ions, resulting in accumulations of ions and charged vacancies at the MAPbI3/electrode interfaces to cause the band bending and change of electric material properties. We have made the first observation that single-digit photon absorption can alter the macroscopic electric and optoelectronic properties of a perovskite thin film.

Room-temperature long-wave infrared detector with thin double layers of amorphous germanium and amorphous silicon.

A longwave-infrared photodetector made of double layers of 100nm amorphous germanium (a-Ge) and 25nm amorphous silicon (a-Si) have been demonstrated. Under room temperature, the device shows the responsivity of 1.7 A/W, detectivity of 6×108 Jones, and noise equivalent power (NEP) of 5pW/√Hz under 5V bias and at 20kHz operation. Studies of frequency dependent characteristics and device modeling indicate that, above 100Hz or beyond the bandwidth of thermal response, the device operates as a quantum detector having the photoelectrons produced by optical excitation from the bandtail states to the mobile states of a-Ge. The superior device performance may be attributed to the combination of two amplification mechanisms: photoconductive gain in a-Ge and cycling excitation process (CEP) in a-Si, with the latter being the dominant factor. Besides its attractive performance, the device has a simple structure and is easy to fabricate at low cost, thus holding promise for night vision, sensing, autonomous driving, and many other applications.

Machine Learning Based Real-Time Image-Guided Cell Sorting and Classification.

Cell classification based on phenotypical, spatial, and genetic information greatly advances our understanding of the physiology and pathology of biological systems. Technologies derived from next generation sequencing and fluorescent activated cell sorting are cornerstones for cell- and genomic-based assays supporting cell classification and mapping. However, there exists a deficiency in technology space to rapidly isolate cells based on high content image information. Fluorescence-activated cell sorting can only resolve cell-to-cell variation in fluorescence and optical scattering. Utilizing microfluidics, photonics, computation microscopy, real-time image processing and machine learning, we demonstrate an image-guided cell sorting and classification system possessing the high throughput of flow cytometer and high information content of microscopy. We demonstrate the utility of this technology in cell sorting based on (1) nuclear localization of glucocorticoid receptors, (2) particle binding to the cell membrane, and (3) DNA damage induced γ-H2AX foci. © 2019 International Society for Advancement of Cytometry.

Review: imaging technologies for flow cytometry.

High-throughput single cell imaging is a critical enabling and driving technology in molecular and cellular biology, biotechnology, medicine and related areas. Imaging flow cytometry combines the single-cell imaging capabilities of microscopy with the high-throughput capabilities of conventional flow cytometry. Recent advances in imaging flow cytometry are remarkably revolutionizing single-cell analysis. This article describes recent imaging flow cytometry technologies and their challenges.

Computational cell analysis for label-free detection of cell properties in a microfluidic laminar flow.

Although a flow cytometer, being one of the most popular research and clinical tools for biomedicine, can analyze cells based on the cell size, internal structures such as granularity, and molecular markers, it provides little information about the physical properties of cells such as cell stiffness and physical interactions between the cell membrane and fluid. In this paper, we propose a computational cell analysis technique using cells' different equilibrium positions in a laminar flow. This method utilizes a spatial coding technique to acquire the spatial position of the cell in a microfluidic channel and then uses mathematical algorithms to calculate the ratio of cell mixtures. Most uniquely, the invented computational cell analysis technique can unequivocally detect the subpopulation of each cell type without labeling even when the cell type shows a substantial overlap in the distribution plot with other cell types, a scenario limiting the use of conventional flow cytometers and machine learning techniques. To prove this concept, we have applied the computation method to distinguish live and fixed cancer cells without labeling, count neutrophils from human blood, and distinguish drug treated cells from untreated cells. Our work paves the way for using computation algorithms and fluidic dynamic properties for cell classification, a label-free method that can potentially classify over 200 types of human cells. Being a highly cost-effective cell analysis method complementary to flow cytometers, our method can offer orthogonal tests in companion with flow cytometers to provide crucial information for biomedical samples.