Development of novel series and parallel sensing system based on nanostructured surface enhanced Raman scattering substrate for biomedical application

With the advance of nanofabrication, the capability of nanoscale metallic structure fabrication opens a whole new study in nanoplasmonics, which is defined as the investigation of photon-electron interaction in the vicinity of nanoscale metallic structures. The strong oscillation of free electrons at the interface between metal and surrounding dielectric material caused by propagating surface plasmon resonance (SPR) or localized surface plasmon resonance (LSPR) enables a variety of new applications in different areas, especially biological sensing techniques. One of the promising biological sensing applications by surface resonance polariton is surface enhanced Raman spectroscopy (SERS), which significantly reinforces the feeble signal of traditional Raman scattering by at least 104 times. It enables highly sensitive and precise molecule identification with the assistance of a SERS substrate. Until now, the design of new SERS substrate fabrication process is still thriving since no dominant design has emerged yet. The ideal process should be able to achieve both a high sensitivity and low cost device in a simple and reliable way. In this thesis two promising approaches for fabricating nanostructured SERS substrate are proposed: thermal dewetting technique and nanoimprint replica technique. These two techniques are demonstrated to show the capability of fabricating high performance SERS substrate in a reliable and cost efficient fashion. In addition, these two techniques have their own unique characteristics and can be integrated with other sensing techniques to build a serial or parallel sensing system. The breakthrough of a combination system with different sensing techniques overcomes the inherent limitations of SERS detection and leverages it to a whole new level of systematic sensing. The development of a sensing platform based on thermal dewetting technique is covered as the first half of this thesis. The process optimization, selection of substrate material, and improved deposition technique are discussed in detail. Interesting phenomena have been found including the influence of Raman enhancement on substrate material selection and hot-spot rich bimetallic nanostructures by physical vapor deposition on metallic seed array, which are barely discussed in past literature but significantly affect the performance of SERS substrate. The optimized bimetallic backplane assisted resonating nanoantenna (BARNA) SERS substrate is demonstrated with the enhancement factor (EF) of 5.8 × 108 with 4.7 % relative standard deviation. By serial combination with optical focusing from nanojet effect, the nanojet and surface enhanced Raman scattering (NASERS) are proved to provide more than three orders of enhancement and enable us to perform stable, nearly single molecule detection. The second part of this thesis includes the development of a parallel dual functional nano Lycurgus cup array (nanoLCA) plasmonic device fabricated by nanoimprint replica technique. The unique configuration of the periodic nanoscale cup-shaped substrate enables a novel hybrid resonance coupling between SPR from extraordinary (EOT) and LSPR from dense sidewall metal nanoparticles with only single deposition process. The sub-50nm dense sidewall metal nanoparticles lead to high SERS performance in solution based detection, by which most biological and chemical analyses are typically performed. The SERS EF was calculated as 2.8 × 107 in a solution based environment with 10.2 % RSD, which is so far the highest reported SERS enhancement achieved with similar periodic EOT devices. In addition, plasmonic colorimetric sensing can be achieved in the very same device and the sensitivity was calculated as 796 nm/RIU with the FOM of 12.7. It creates a unique complementary sensing platform with both rapid on-site colorimetric screening and follow-up precise Raman analysis for point of care and resource limited environment applications. The implementations of bifunctional sensing on opto-microfluidic and smartphone platforms are proposed and examined here as well

Nanotargeted Radionuclides for Cancer Nuclear Imaging and Internal Radiotherapy

Current progress in nanomedicine has exploited the possibility of designing tumor-targeted nanocarriers being able to deliver radionuclide payloads in a site or molecular selective manner to improve the efficacy and safety of cancer imaging and therapy. Radionuclides of auger electron-, α-, β-, and γ-radiation emitters have been surface-bioconjugated or after-loaded in nanoparticles to improve the efficacy and reduce the toxicity of cancer imaging and therapy in preclinical and clinical studies. This article provides a brief overview of current status of applications, advantages, problems, up-to-date research and development, and future prospects of nanotargeted radionuclides in cancer nuclear imaging and radiotherapy. Passive and active nanotargeting delivery of radionuclides with illustrating examples for tumor imaging and therapy are reviewed and summarized. Research on combing different modes of selective delivery of radionuclides through nanocarriers targeted delivery for tumor imaging and therapy offers the new possibility of large increases in cancer diagnostic efficacy and therapeutic index. However, further efforts and challenges in preclinical and clinical efficacy and toxicity studies are required to translate those advanced technologies to the clinical applications for cancer patients

A Shallow Ritz Method for Elliptic Problems with Singular Sources

In this paper, a shallow Ritz-type neural network for solving elliptic equations with delta function singular sources on an interface is developed. There are three novel features in the present work; namely, (i) the delta function singularity is naturally removed, (ii) level set function is introduced as a feature input, (iii) it is completely shallow, comprising only one hidden layer. We first introduce the energy functional of the problem and then transform the contribution of singular sources to a regular surface integral along the interface. In such a way, the delta function singularity can be naturally removed without introducing a discrete one that is commonly used in traditional regularization methods, such as the well-known immersed boundary method. The original problem is then reformulated as a minimization problem. We propose a shallow Ritz-type neural network with one hidden layer to approximate the global minimizer of the energy functional. As a result, the network is trained by minimizing the loss function that is a discrete version of the energy. In addition, we include the level set function of the interface as a feature input of the network and find that it significantly improves the training efficiency and accuracy. We perform a series of numerical tests to show the accuracy of the present method and its capability for problems in irregular domains and higher dimensions

Environment Diversification with Multi-head Neural Network for Invariant Learning

Neural networks are often trained with empirical risk minimization; however, it has been shown that a shift between training and testing distributions can cause unpredictable performance degradation. On this issue, a research direction, invariant learning, has been proposed to extract invariant features insensitive to the distributional changes. This work proposes EDNIL, an invariant learning framework containing a multi-head neural network to absorb data biases. We show that this framework does not require prior knowledge about environments or strong assumptions about the pre-trained model. We also reveal that the proposed algorithm has theoretical connections to recent studies discussing properties of variant and invariant features. Finally, we demonstrate that models trained with EDNIL are empirically more robust against distributional shifts.Comment: In Proceedings of 36th Conference on Neural Information Processing Systems (NeurIPS 2022

Total Parenteral Nutrition Treatment in a Diabetic Pregnant Woman Complicated with Hyperemesis Gravidarum, Malnutrition, and Intrauterine Growth Restriction

SummaryObjectiveWe report a case of a pregnant woman with diabetes, complicated with hyperemesis gravidarum, malnutrition, and intrauterine growth restriction, who was treated beneficially with total parenteral nutrition.Case ReportA 26-year-old diabetic nullipara complained of severe nausea and vomiting from early in her pregnancy. Malnutrition developed at about 24 weeks of gestation. In spite of treatment with antiemetics, antihistamine, and vitamin B6, the symptoms persisted, and intrauterine growth restriction eventually developed. Total parenteral nutrition was commenced at 29 weeks of gestation. She delivered a healthy female neonate with a birth body weight of 2,120 g at 36 weeks of gestation by cesarean section. Total parenteral nutrition was gradually tapered off 1 week later, and the mother was discharged from hospital in good condition 10 days after delivery.ConclusionTotal parenteral nutrition may be used to treat diabetic patients with hyperemesis gravidarum who develop malnutrition causing intrauterine growth restriction