Spatio-spectral 4D coherent ranging using a flutter-wavelength-swept laser.

Coherent light detection and ranging (LiDAR), particularly the frequency-modulated continuous-wave LiDAR, is a robust optical imaging technology for measuring long-range distance and velocity in three dimensions (3D). We propose a spatio-spectral coherent LiDAR based on a unique wavelength-swept laser to enable both axial coherent ranging and lateral spatio-spectral beam scanning simultaneously. Instead of the conventional unidirectional wavelength-swept laser, a flutter-wavelength-swept laser (FWSL) successfully decoupled bidirectional wavelength modulation and continuous wavelength sweep, which overcame the measurable distance limited by the sampling process. The decoupled operation in FWSL enabled sequential sampling of flutter-wavelength modulation across its wide spectral bandwidth of 160 nm and, thus, allowed simultaneous distance and velocity measurement over an extended measurable distance. Herein, complete four-dimensional (4D) imaging, combining real-time 3D distance and velocity measurements, was implemented by solid-state beam scanning. An acousto-optic scanner was synchronized to facilitate the other lateral beam scanning, resulting in an optimized solid-state coherent LiDAR system. The proposed spatio-spectral coherent LiDAR system achieved high-resolution coherent ranging over long distances and real-time 4D imaging with a frame rate of 10 Hz, even in challenging environments.

Classification of Diffuse Glioma Subtype from Clinical-Grade Pathological Images Using Deep Transfer Learning.

Diffuse gliomas are the most common primary brain tumors and they vary considerably in their morphology, location, genetic alterations, and response to therapy. In 2016, the World Health Organization (WHO) provided new guidelines for making an integrated diagnosis that incorporates both morphologic and molecular features to diffuse gliomas. In this study, we demonstrate how deep learning approaches can be used for an automatic classification of glioma subtypes and grading using whole-slide images that were obtained from routine clinical practice. A deep transfer learning method using the ResNet50V2 model was trained to classify subtypes and grades of diffuse gliomas according to the WHO's new 2016 classification. The balanced accuracy of the diffuse glioma subtype classification model with majority voting was 0.8727. These results highlight an emerging role of deep learning in the future practice of pathologic diagnosis.

Micro gas preconcentrator using metal organic framework embedded metal foam for detection of low-concentration volatile organic compounds.

Analysis of volatile organic compounds (VOCs) is essential for on-site environmental monitoring and toxic chemicals detection. However, quantitatively detecting VOC gases is difficult because of their low gas concentration (<100 ppb), and preconcentration is necessary to overcome the detection limitations of various gas sensors. Many studies on micro preconcentrators (µ-PC) have been reported, however, these devices suffer from high desorption temperatures and significant pressure drops, which degrade sensing ability and increase operating costs, respectively. Due to these disadvantages, such devices are not yet commercially available. In this study, a µ-PC was developed using metal organic framework embedded metal foam (MOFM) as an adsorbent. The preconcentration performance of the µ-PC was evaluated based on several key parameters, such as desorption temperature, adsorption time, and initial sample concentration. In addition, the MOFM and commercial adsorbents were each packed in the same µ-PC chip, respectively, to compare their preconcentration and pressure drop performances. The MOFM-adsorbent-packed µ-PC demonstrated the preconcentration factors were 2.6 and 4 times higher, and the pressure drops were 4 and 3 times lower than those of the commercial adsorbents under the same conditions owing to the high specific surface area and the efficient flow distribution of the MOFM adsorbent.

Development of Open-Tubular-Type Micro Gas Chromatography Column with Bump Structures.

Gas chromatography (GC) is the chemical analysis technique most widely used to separate and identify gas components, and it has been extensively applied in various gas analysis fields such as non-invasive medical diagnoses, indoor air quality monitoring, and outdoor environmental monitoring. Micro-electro-mechanical systems (MEMS)-based GC columns are essential for miniaturizing an integrated gas analysis system (Micro GC system). This study reports an open-tubular-type micro GC (µ-GC) column with internal bump structures (bump structure µ-GC column) that substantially increase the interaction between the gas mixture and a stationary phase. The developed bump structure µ-GC column, which was fabricated on a 2 cm × 2 cm µ-GC chip and coated with a non-polar stationary phase, is 1.5 m-long, 150 µm-wide, and 400 µm-deep. It has an internal microfluidic channel in which the bumps, which are 150 µm diameter half-circles, are alternatingly disposed to face each other on the surface of the microchannel. The fabricated bump structure µ-GC column yielded a height-equivalent-to-a-theoretical-plate (HETP) of 0.009 cm (11,110 plates/m) at an optimal carrier gas velocity of 17 cm/s. The mechanically robust bump structure µ-GC column proposed in this study achieved higher separation efficiency than a commercially available GC column and a typical µ-GC column with internal post structures classified as a semi-packed-type column. The experimental results demonstrate that the developed bump structure µ-GC column can separate a gas mixture completely, with excellent separation resolution for formaldehyde, benzene, toluene, ethylbenzene, and xylene mixture, under programmed operating temperatures.

CNT Foam-Embedded Micro Gas Preconcentrator for Low-Concentration Ethane Measurements.

Breath analysis has become increasingly important as a noninvasive process for the clinical diagnosis of patients suffering from various diseases. Many commercial gas preconcentration instruments are already being used to overcome the detection limits of commercial gas sensors for gas concentrations which are as low as ~100 ppb in exhaled breath. However, commercial instruments are large and expensive, and they require high power consumption and intensive maintenance. In the proposed study, a micro gas preconcentrator (µ-PC) filled with a carbon nanotube (CNT) foam as an adsorbing material was designed and fabricated for the detection of low-concentration ethane, which is known to be one of the most important biomarkers related to chronic obstructive pulmonary disease (COPD) and asthma. A comparison of the performance of two gas-adsorbing materials, i.e., the proposed CNT foam and commercial adsorbing material, was performed using the developed µ-PC. The experimental results showed that the synthesized CNT foam performs better than a commercial adsorbing material owing to its lower pressure drop and greater preconcentration efficiency in the µ-PC. The present results show that the application of CNT foam-embedded µ-PC in portable breath analysis systems holds great promise.

Highly Increased Flow-Induced Power Generation on Plasmonically Carbonized Single-Walled Carbon Nanotube .

We generate networks and carbonization between individualized single-walled carbon nanotubes (SWCNTs) by an optimized plasmonic heating process using a halogen lamp to improve electrical properties for flow-induced energy harvesting. These properties were characterized by Raman spectra, a field-emission-scanning probe, transmission electron microscopy, atomic force microscopy and thermographic camera. The electrical sheet resistance of carbonized SWCNTs was decreased to 2.71 kΩ/□, 2.5 times smaller than normal-SWCNTs. We demonstrated flow-induced voltage generation on SWCNTs at various ion concentrations of NaCl. The generated voltage and current for the carbonized-SWCNTs were 9.5 and 23.5 times larger than for the normal-SWCNTs, respectively, based on the electron dragging mechanism.