Browsing by Author "Zhao, Xuan"

Now showing 1 - 5 of 5
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    Characterizations of two-photon absorption process induced by defects in aluminum nitride using Z-scan method
    (IOP Publishing, 2024) Zhou, Jingan; Li, Tao; Zhao, Xuan; Zhang, Xiang; Doumani, Jacques; Xu, Mingfei; He, Ziyi; Luo, Shisong; Mei, Zhaobo; Chang, Cheng; Robinson, Jacob T.; Ajayan, Pulickel M.; Kono, Junichiro; Zhao, Yuji; Smalley-Curl Institute
    In this work, we reported two-photon absorption (TPA) measurements for aluminum vacancies in Aluminum nitride single crystals. We measured the linear transmission and identified the defect levels. Using the Z-scan method, we measured the TPA coefficients of the transitions between defect levels from 380 nm to 735 nm. The transition occurs between the aluminum vacancies defect levels. Furthermore, the power dependence shows good linear fitting, confirming the TPA mechanism. These results will be helpful for the design and fabrication of ultra-low loss waveguides and integrated photonics in the ultraviolet spectral range.
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    Deep learning extended depth-of-field microscope for fast and slide-free histology
    (PNAS, 2020) Jin, Lingbo; Tang, Yubo; Wu, Yicheng; Coole, Jackson B.; Tan, Melody T.; Zhao, Xuan; Badaoui, Hawraa; Robinson, Jacob T.; Williams, Michelle D.; Gillenwater, Ann M.; Richards-Kortum, Rebecca R.; Veeraraghavan, Ashok
    Microscopic evaluation of resected tissue plays a central role in the surgical management of cancer. Because optical microscopes have a limited depth-of-field (DOF), resected tissue is either frozen or preserved with chemical fixatives, sliced into thin sections placed on microscope slides, stained, and imaged to determine whether surgical margins are free of tumor cells—a costly and time- and labor-intensive procedure. Here, we introduce a deep-learning extended DOF (DeepDOF) microscope to quickly image large areas of freshly resected tissue to provide histologic-quality images of surgical margins without physical sectioning. The DeepDOF microscope consists of a conventional fluorescence microscope with the simple addition of an inexpensive (less than $10) phase mask inserted in the pupil plane to encode the light field and enhance the depth-invariance of the point-spread function. When used with a jointly optimized image-reconstruction algorithm, diffraction-limited optical performance to resolve subcellular features can be maintained while significantly extending the DOF (200 µm). Data from resected oral surgical specimens show that the DeepDOF microscope can consistently visualize nuclear morphology and other important diagnostic features across highly irregular resected tissue surfaces without serial refocusing. With the capability to quickly scan intact samples with subcellular detail, the DeepDOF microscope can improve tissue sampling during intraoperative tumor-margin assessment, while offering an affordable tool to provide histological information from resected tissue specimens in resource-limited settings.
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    DeepDOF-SE: affordable deep-learning microscopy platform for slide-free histology
    (Springer Nature, 2024) Jin, Lingbo; Tang, Yubo; Coole, Jackson B.; Tan, Melody T.; Zhao, Xuan; Badaoui, Hawraa; Robinson, Jacob T.; Williams, Michelle D.; Vigneswaran, Nadarajah; Gillenwater, Ann M.; Richards-Kortum, Rebecca R.; Veeraraghavan, Ashok
    Histopathology plays a critical role in the diagnosis and surgical management of cancer. However, access to histopathology services, especially frozen section pathology during surgery, is limited in resource-constrained settings because preparing slides from resected tissue is time-consuming, labor-intensive, and requires expensive infrastructure. Here, we report a deep-learning-enabled microscope, named DeepDOF-SE, to rapidly scan intact tissue at cellular resolution without the need for physical sectioning. Three key features jointly make DeepDOF-SE practical. First, tissue specimens are stained directly with inexpensive vital fluorescent dyes and optically sectioned with ultra-violet excitation that localizes fluorescent emission to a thin surface layer. Second, a deep-learning algorithm extends the depth-of-field, allowing rapid acquisition of in-focus images from large areas of tissue even when the tissue surface is highly irregular. Finally, a semi-supervised generative adversarial network virtually stains DeepDOF-SE fluorescence images with hematoxylin-and-eosin appearance, facilitating image interpretation by pathologists without significant additional training. We developed the DeepDOF-SE platform using a data-driven approach and validated its performance by imaging surgical resections of suspected oral tumors. Our results show that DeepDOF-SE provides histological information of diagnostic importance, offering a rapid and affordable slide-free histology platform for intraoperative tumor margin assessment and in low-resource settings.
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    Near-Field Sensing of Single Cell Activities Using Visible-Range Micro-Ring Resonators
    (2021-07-26) Zhao, Xuan; Robinson, Jacob T.; Veeraraghavan, Ashok; Tkaczyk, Tomasz S.
    The development of optogenetic actuators as well as genetically encoded fluorescent indicators provide powerful resources to advance the study of the brain. Moreover, optogenetic control combined with optical recording has a huge potential on achieving all-optical-based, high-speed closed-loop neuromodulations with cellular and biomolecular specificity, which can be critical to many areas of studies, such as brain dynamics, reverse-engineering of neural circuits, and treatments for neurological diseases. However, achieving all-optical interrogation requires optical tools and sensors with high spatiotemporal control and resolution, yet traditional optical setups suffer from light penetration depth and their large form factors, which limit their extent of reach as well as the SNR. In this thesis, we propose to develop a high-fidelity biohybrid sensor based on optical measurements of visible-range micro-ring resonators (MRRs). Through FDTD simulations, we show that the designed MRR is sufficiently sensitive to detect action potentials of an individual cell via absorption measurements. For proof-of-principle validation, we fabricate the MRR sensors, and design and construct a customized biophotonic experimental platform to characterize the MRR sensors and perform in-vitro experiments. Thanks to integrated photonics, the sensitivity and scalability of this geometry could potentially enable optical recording of many individual cells across large physical areas. The combination of our proposed MRR sensors with the existing integrated photonic optogenetic stimulators could provide a possible solution to the future’s miniaturized and scalable neural interface for all-optical-based, closed-loop neuromodulation.
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    Rigid and Flexible Integrated Photonics for Optical Biosensing
    (2023-11-20) Zhao, Xuan; Robinson, Jacob
    Biosensing of physiological signals, such as the body temperature and biomolecules, are of critical importance to disease monitoring, diagnosis, and treatment, in both clinical and research settings. Optical biosensors in particular have shown unique advantages when compared with traditional electrochemical sensors—optical sensors are electromagnetic interference (EMI) free, resistant to electroactive interferants, while exhibiting both sensitivity and specificity. Integrated photonics based on the refractive index sensing mechanism is one promising example of compact optical biosensing, with high sensitivity, specificity, and label-free operation. Moreover, these integrated photonic sensors can have ultrasmall form factors and be manufactured at low costs, thanks to their CMOS-fabrication compatibility. In the first part of the thesis, we demonstrate a rigid, silicon-on-insulator-based integrated photonic biosensor for high-sensitivity glucose detection. We show that by functionalization of receptors on a micro-ring resonator (MRR) sensor surface, the MRR sensor is able to reach the limit of detection for non-invasive glucose sensing in saliva and tears. Moreover, we show that the MRR sensor responds minimally to common interferents present in biofluids and performs stably across a wide pH range compared with enzyme-based electrochemical sensors. These results could potentially facilitate the development of a low-cost, benchtop optical platform for non-invasive diabetes screening. Although SOI-based integrated photonic sensors can detect biomolecules with a high LOD and specificity, the sensor’s mechanical rigidity has largely limited its applications to in vitro, benchtop settings. In the second part of the thesis, we demonstrate a flexible integrated photonic platform that is better suited for in vivo biological applications. We show that by utilizing TiO2 as the core material and SU-8 polymer as the flexible cladding, the flexible photonic MRR sensors are capable of high-sensitivity temperature sensing. More importantly, we show that the flexible photonic sensor can be surface-functionalized through a generalized, polymer-compatible approach for biochemical detection with sub-uM sensitivity. The successful demonstration of our sensors is a key step toward developing more biocompatible, conformal, and flexible photonic platforms for next-generation biosensing applications that require tissue contact or device implantation.