Modeling of plasmonic properties of nanostructures for next generation solar cells and beyond

Plasmonic particles and nanostructures are widely used in photovoltaic and photonics. Surface plasmons were found to enhance different types of solar cells including plasmonic DSSCs, plasmonic solid semiconductor solar cells, plasmonic organic solar cells, and plasmonic perovskite solar cell. Size, composition, and shape of plasmonic nanoparticles as well as nanometer-distance control between particles are key design factors of plasmonic nanostructures. Modeling is rapidly gaining in importance for mechanistic understanding and rational design of plasmonic nanostructures. We review the modeling approaches used to model plasmon resonance features of nanostructures, from classical approaches that can routinely handle most particle sizes used in solar cells to approaches beyond classical electrodynamics such as ab initio approaches based on time-dependent density functional theory (TD-DFT). We highlight recently emerging approaches which have the potential to significantly enhance modeling capabilities in the coming years, in particular, by allowing atomistic (ab initio) modeling at realistic length scales, i.e. of particle sizes beyond 10 nm which are of most interest to plasmonic solar cells but remain problematic with traditional DFT-based techniques, such as density functional tight binding (DFTB) based approaches, time-dependent orbital-free DFT, and machine learning-based approaches, as well as many-body perturbation theory which is expected to gain usage with advances in computing power

Ultrafast Microfluidic Immunoassays Towards Real-time Intervention of Cytokine Storms

Biomarker-guided precision medicine holds great promise to provide personalized therapy with a good understanding of the molecular or cellular data of an individual patient. However, implementing this approach in critical care uniquely faces enormous challenges as it requires obtaining “real-time” data with high sensitivity, reliability, and multiplex capacity near the patient’s bedside in the quickly evolving illness. Current immunodiagnostic platforms generally compromise assay sensitivity and specificity for speed or face significantly increased complexity and cost for highly multiplexed detection with low sample volume. This thesis introduces two novel ultrafast immunoassay platforms: one is a machine learning-based digital molecular counting assay, and the other is a label-free nano-plasmonic sensor integrated with an electrokinetic mixer. Both of them incorporate microfluidic approaches to pave the way for near-real-time interventions of cytokine storms. In the first part of the thesis, we present an innovative concept and the theoretical study that enables ultrafast measurement of multiple protein biomarkers (<1 min assay incubation) with comparable sensitivity to the gold standard ELISA method. The approach, which we term “pre-equilibrium digital enzyme-linked immunosorbent assay” (PEdELISA) incorporates the single-molecular counting of proteins at the early, pre-equilibrium state to achieve the combination of high speed and sensitivity. We experimentally demonstrated the assay’s application in near-real-time monitoring of patients receiving chimeric antigen receptor (CAR) T-cell therapy and for longitudinal serum cytokine measurements in a mouse sepsis model. In the second part, we report the further development of a machine learning-based PEdELISA microarray data analysis approach with a significantly extended multiplex capacity using the spatial-spectral microfluidic encoding technique. This unique approach, together with a convolutional neural network-based image analysis algorithm, remarkably reduced errors faced by the highly multiplexed digital immunoassay at low analyte concentrations. As a result, we demonstrated the longitudinal data collection of 14 serum cytokines in human patients receiving CAR-T cell therapy at concentrations < 10pg/mL with a sample volume < 10 µL and 5-min assay incubation. In the third part, we demonstrate the clinical application of a machine learning-based digital protein microarray platform for rapid multiplex quantification of cytokines from critically ill COVID-19 patients admitted to the intensive care unit. The platform comprises two low-cost modules: (i) a semi-automated fluidic dispensing module that can be operated inside a biosafety cabinet to minimize the exposure of technician to the virus infection and (ii) a compact fluorescence optical scanner for the potential near-bedside readout. The automated system has achieved high interassay precision (~10% CV) with high sensitivity (<0.4pg/mL). Our data revealed large subject-to-subject variability in patient responses to anti-inflammatory treatment for COVID-19, reaffirming the need for a personalized strategy guided by rapid cytokine assays. Lastly, an AC electroosmosis-enhanced localized surface plasmon resonance (ACE-LSPR) biosensing device was presented for rapid analysis of cytokine IL-1β among sepsis patients. The ACE-LSPR device is constructed using both bottom-up and top-down sensor fabrication methods, allowing the seamless integration of antibody-conjugated gold nanorod (AuNR) biosensor arrays with microelectrodes on the same microfluidic platform. Applying an AC voltage to microelectrodes while scanning the scattering light intensity variation of the AuNR biosensors results in significantly enhanced biosensing performance. The technologies developed have enabled new capabilities with broad application to advance precision medicine of life-threatening acute illnesses in critical care, which potentially will allow the clinical team to make individualized treatment decisions based on a set of time-resolved biomarker signatures.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163129/1/yujing_1.pd

Computer-Aided Design and Analysis of Spectrally Aligned Hybrid Plasmonic Nanojunctions for SERS Detection of Nucleobases

Hybrid plasmonic nanojunctions with optimal surface-enhanced Raman scattering (SERS) activity are designed via a computer-aided approach, and fabricated via time-controlled aqueous self-assembly of core@shell gold@silver nanoparticles (Au@Ag NPs) with cucurbit[7]uril (CB7) upon simple mixing. The authors showed that SERS signals can be significantly boosted by the incorporation of a strong plasmonic metal and the spectral alignment between the maximal localized surface plasmon resonance (LSPR) and a laser wavelength used for SERS excitation. In a proof-of-concept application, SERS detection of nucleobases with a 633-nm laser has been demonstrated by positioning them within the nanojunctions via formation of host–guest complexes with CB7, achieving rapid response with a detection limit down to sub-nanomolar concentration and an enhancement factor (EF) up to ≈109–1010, i.e., the minimum required EF for single-molecule detection. Furthermore, machine-learning-driven multiplexing of nucleobases is demonstrated, which shows promise in point-of-care diagnosis of diseases related to oxidative damage of DNA and wastewater-based epidemiology

Design and Fabrication of Integrated Plasmonic Platforms for Ultra-sensitive Molecular and Biomolecular Detections

One of the major challenges in analytical and biological sciences is to develop a device to obtain unambiguous chemical and structural properties of a material or a probe biomolecule with high reproducibility and ultra-high sensitivity. Moreover, in addition to such a high sensitivity, other cases such as minimum intrusiveness, small amounts of analyte, and short acquisition time and high reproducibility are key parameters that can be valued in any analytical measurements. Among the promising methods to achieve such endeavor, plasmon-mediated surface-enhanced spectroscopic techniques, such as surface-enhanced Raman spectroscopy (SERS), are considered as suitable options. Such techniques take advantage of the interaction between an optical field and a metallic nanostructure to magnify the electromagnetic (EM) field in the proximity of the nanostructure. This results in an amplified signal of the vibrational fingerprints of the adsorbed probe molecules onto the metallic surface. Keys to obtaining ultra-sensitive SERS measurements are the development of rationally-designed plasmonic nanostructures. Besides, a major challenge for controlled and reliable sensitive measurements of probe biomolecules on biological cells gives rise due to the intrinsic random positioning and proliferation of these cells over a substrate such as a glass coverslip. In this thesis, the rational design and development of a fluorocarbon thin film micropatterned platform is introduced for controlled programming of conventional and transfected cells proliferation over the substrate. They also provided high throughput capability of controlled neuronal network connections towards advanced imaging and sensitive detection of biomolecules of interest at nanoscale resolution. This micropatterned platform was also integrated with optimized plasmonic nanostructures fabricated by nanosphere lithography (NSL) for SERS biosensing of glycans using a Raman reporter over the positionally-controlled single cells surfaces. In addition to providing controlled plasmon-mediated measurements, the fabrications of two newly-developed 3D plasmonic nanostructures have been introduced in this thesis. These are nanopyramids arrays fabricated by NSL and arrays of nanoholes with co-registered nanocones fabricated by electron-beam lithography (EBL). These approaches have been used not only for ultra-sensitive molecular detection at the monolayer level in a variety of configurations, but also towards label-free single molecule detection at sub-femtomolar concentrations

Parametric Optimization of Visible Wavelength Gold Lattice Geometries for Improved Plasmon-Enhanced Fluorescence Spectroscopy

The exploitation of spectro-plasmonics will allow for innovations in optical instrumentation development and the realization of more efficient optical biodetection components. Biosensors have been shown to improve the overall quality of life through real-time detection of various antibody-antigen reactions, biomarkers, infectious diseases, pathogens, toxins, viruses, etc. has led to increased interest in the research and development of these devices. Further advancements in modern biosensor development will be realized through novel electrochemical, electromechanical, bioelectrical, and/or optical transduction methods aimed at reducing the size, cost, and limit of detection (LOD) of these sensor systems. One such method of optical transduction involves the exploitation of the plasmonic resonance of noble metal nanostructures. This thesis presents the optimization of the electric (E) field enhancement granted from localized surface plasmon resonance (LSPR) via parametric variation of periodic gold lattice geometries using finite difference time domain (FDTD) software. Comprehensive analyses of cylindrical, square, star, and triangular lattice feature geometries were performed to determine the largest surface E-field enhancement resulting from LSPR for reducing the LOD of plasmon-enhanced fluorescence (PEF). The design of an optical transducer engineered to yield peak E-field enhancement and, therefore, peak excitation enhancement of fluorescent labels would enable for improved emission enhancement of these labels. The methodology presented in this thesis details the optimization of plasmonic lattice geometries for improving current visible wavelength fluorescence spectroscopy

Advances in Unconventional Lithography

The term Lithography encompasses a range of contemporary technologies for micro and nano scale fabrication. Originally driven by the evolution of the semiconductor industry, lithography has grown from its optical origins to demonstrate increasingly fine resolution and to permeate fields as diverse as photonics and biology. Today, greater flexibility and affordability are demanded from lithography more than ever before. Diverse needs across many disciplines have produced a multitude of innovative new lithography techniques. This book, which is the final instalment in a series of three, provides a compelling overview of some of the recent advances in lithography, as recounted by the researchers themselves. Topics discussed include nanoimprinting for plasmonic biosensing, soft lithography for neurobiology and stem cell differentiation, colloidal substrates for two-tier self-assembled nanostructures, tuneable diffractive elements using photochromic polymers, and extreme-UV lithography

Random scattering of surface plasmons for sensing and tracking

In this thesis, a single particle biosensing setup, capable of sensing and tracking single nanoscale biological particles, is proposed and investigated theoretically. The setup is based on monitoring the speckle pattern intensity distribution arising due to random scattering of surface plasmon polaritons (SPPs) from a metal surface. An analyte particle close to the surface will additionally scatter light, perturbing the speckle pattern. From this speckle pattern perturbation, the analyte particle can be detected and tracked. Theoretical sensitivity analysis predicts a biological particle on the order of 10nm in radius gives a fractional intensity perturbation to the speckle intensity of 10^4, comparable to intensity contrasts used in existing interferometric scattering sensing techniques. A formula for the minimum detectable particle size is derived. In addition, an algorithm is derived capable of extracting the particle trajectory in the single scattering regime from the change to the speckle intensity perturbation over time and shown to be capable of errors of approximately 1nm on simulated data under optimal noise conditions. The effect of multiple scattering on the speckle pattern perturbation is studied, and it is shown that, by tuning the scattering mean free path and individual scatterer properties of a random nanostructure of scatterers on the metal surface, one can increase the magnitude of the speckle field perturbation by up to the order of 10^2. A neural network based localisation algorithm is developed to calculate the analyte particle position based on the speckle intensity perturbation and its performance on simulated data is studied. Mean errors on the order of 20nm were found, depending on the size of the region over which the particle must be tracked. Unlike the single scattering tracking algorithm, the neural network algorithm continues to function in the multiple scattering regime.Open Acces