Terahertz time domain spectroscopy of amino acids and sugars

A time-domain spectrometer for use in the terahertz (THz) spectral range was designed and constructed. Due to there being few existing methods of generating and detecting THz radiation, the spectrometer is expected to have vast applications to solid, liquid, and gas phase samples. In particular, knowledge of complex organic chemistry and chemical abundances in the interstellar medium (ISM) can be obtained when compared to astronomical data. The THz spectral region is of particular interest due to reduced line density when compared to the millimeter wave spectrum, the existence of high resolution observatories, and potentially strong transitions resulting from the lowest-lying vibrational modes of large molecules.

The heart of the THz time-domain spectrometer (THz-TDS) is the ultrafast laser. Due to the femtosecond duration of ultrafast laser pulses and an energy-time uncertainty relationship, the pulses typically have a several-THz bandwidth. By various means of optical rectification, the optical pulse carrier envelope shape, i.e. intensity-time profile, can be transferred to the phase of the resulting THz pulse. As a consequence, optical pump-THz probe spectroscopy is readily achieved, as was demonstrated in studies of dye-sensitized TiO₂, as discussed in chapter 4. Detection of the terahertz radiation is commonly based on electro-optic sampling and provides full phase information. This allows for accurate determination of both the real and imaginary index of refraction, the so-called optical constants, without additional analysis. A suite of amino acids and sugars, all of which have been found in meteorites, were studied in crystalline form embedded in a polyethylene matrix. As the temperature was varied between 10 and 310 K, various strong vibrational modes were found to shift in spectral intensity and frequency. Such modes can be attributed to intramolecular, intermolecular, or phonon modes, or to some combination of the three.

Time-Domain TeraHertz Spectroscopy and Observational Probes of Prebiotic Interstellar Gas and Ice Chemistry

Understanding the origin of life on Earth has long fascinated the minds of the global community, and has been a driving factor in interdisciplinary research for centuries. Beyond the pioneering work of Darwin, perhaps the most widely known study in the last century is that of Miller and Urey, who examined the possibility of the formation of prebiotic chemical precursors on the primordial Earth [1]. More recent studies have shown that amino acids, the chemical building blocks of the biopolymers that comprise life as we know it on Earth, are present in meteoritic samples, and that the molecules extracted from the meteorites display isotopic signatures indicative of an extraterrestrial origin [2]. The most recent major discovery in this area has been the detection of glycine (NH₂CH₂COOH), the simplest amino acid, in pristine cometary samples returned by the NASA STARDUST mission [3]. Indeed, the open questions left by these discoveries, both in the public and scientific communities, hold such fascination that NASA has designated the understanding of our "Cosmic Origins" as a key mission priority.

Despite these exciting discoveries, our understanding of the chemical and physical pathways to the formation of prebiotic molecules is woefully incomplete. This is largely because we do not yet fully understand how the interplay between grain-surface and sub-surface ice reactions and the gas-phase affects astrophysical chemical evolution, and our knowledge of chemical inventories in these regions is incomplete. The research presented here aims to directly address both these issues, so that future work to understand the formation of prebiotic molecules has a solid foundation from which to work.

From an observational standpoint, a dedicated campaign to identify hydroxylamine (NH₂OH), potentially a direct precursor to glycine, in the gas-phase was undertaken. No trace of NH₂OH was found. These observations motivated a refinement of the chemical models of glycine formation, and have largely ruled out a gas-phase route to the synthesis of the simplest amino acid in the ISM. A molecular mystery in the case of the carrier of a series of transitions was resolved using observational data toward a large number of sources, confirming the identity of this important carbon-chemistry intermediate B11244 as l-C₃H⁺ and identifying it in at least two new environments. Finally, the doubly-nitrogenated molecule carbodiimide HNCNH was identified in the ISM for the first time through maser emission features in the centimeter-wavelength regime.

In the laboratory, a TeraHertz Time-Domain Spectrometer was constructed to obtain the experimental spectra necessary to search for solid-phase species in the ISM in the THz region of the spectrum. These investigations have shown a striking dependence on large-scale, long-range (i.e. lattice) structure of the ices on the spectra they present in the THz. A database of molecular spectra has been started, and both the simplest and most abundant ice species, which have already been identified, as well as a number of more complex species, have been studied. The exquisite sensitivity of the THz spectra to both the structure and thermal history of these ices may lead to better probes of complex chemical and dynamical evolution in interstellar environments.

Advanced Monte Carlo Simulation and Machine Learning for Frequency Domain Optical Coherence Tomography

Optical Coherence Tomography(OCT) is a popular, rapidly growing imaging technique with an increasing number of bio-medical applications due to its noninvasive nature. However, there are three major challenges in understanding and improving an OCT system: (1) Obtaining an OCT image is not easy. It either takes a real medical experiment or requires days of computer simulation. Without much data, it is difficult to study the physical processes underlying OCT imaging of different objects simply because there aren't many imaged objects. (2) Interpretation of an OCT image is also hard. This challenge is more profound than it appears. For instance, it would require a trained expert to tell from an OCT image of human skin whether there is a lesion or not. This is expensive in its own right, but even the expert cannot be sure about the exact size of the lesion or the width of the various skin layers. The take-away message is that analyzing an OCT image even from a high level would usually require a trained expert, and pixel-level interpretation is simply unrealistic. The reason is simple: we have OCT images but not their underlying ground-truth structure, so there is nothing to learn from. (3) The imaging depth of OCT is very limited (millimeter or sub-millimeter on human tissues). While OCT utilizes infrared light for illumination to stay noninvasive, the downside of this is that photons at such long wavelengths can only penetrate a limited depth into the tissue before getting back-scattered. To image a particular region of a tissue, photons first need to reach that region. As a result, OCT signals from deeper regions of the tissue are both weak (since few photons reached there) and distorted (due to multiple scatterings of the contributing photons). This fact alone makes OCT images very hard to interpret.

This thesis addresses the above challenges by successfully developing an advanced Monte Carlo simulation platform which is 10000 times faster than the state-of-the-art simulator in the literature, bringing down the simulation time from 360 hours to a single minute. This powerful simulation tool not only enables us to efficiently generate as many OCT images of objects with arbitrary structure and shape as we want on a common desktop computer, but it also provides us the underlying ground-truth of the simulated images at the same time because we dictate them at the beginning of the simulation. This is one of the key contributions of this thesis. What allows us to build such a powerful simulation tool includes a thorough understanding of the signal formation process, clever implementation of the importance sampling/photon splitting procedure, efficient use of a voxel-based mesh system in determining photon-mesh interception, and a parallel computation of different A-scans that consist a full OCT image, among other programming and mathematical tricks, which will be explained in detail later in the thesis.

Next we aim at the inverse problem: given an OCT image, predict/reconstruct its ground-truth structure on a pixel level. By solving this problem we would be able to interpret an OCT image completely and precisely without the help from a trained expert. It turns out that we can do much better. For simple structures we are able to reconstruct the ground-truth of an OCT image more than 98% correctly, and for more complicated structures (e.g., a multi-layered brain structure) we are looking at 93%. We achieved this through extensive uses of Machine Learning. The success of the Monte Carlo simulation already puts us in a great position by providing us with a great deal of data (effectively unlimited), in the form of (image, truth) pairs. Through a transformation of the high-dimensional response variable, we convert the learning task into a multi-output multi-class classification problem and a multi-output regression problem. We then build a hierarchy architecture of machine learning models (committee of experts) and train different parts of the architecture with specifically designed data sets. In prediction, an unseen OCT image first goes through a classification model to determine its structure (e.g., the number and the types of layers present in the image); then the image is handed to a regression model that is trained specifically for that particular structure to predict the length of the different layers and by doing so reconstruct the ground-truth of the image. We also demonstrate that ideas from Deep Learning can be useful to further improve the performance.

It is worth pointing out that solving the inverse problem automatically improves the imaging depth, since previously the lower half of an OCT image (i.e., greater depth) can be hardly seen but now becomes fully resolved. Interestingly, although OCT signals consisting the lower half of the image are weak, messy, and uninterpretable to human eyes, they still carry enough information which when fed into a well-trained machine learning model spits out precisely the true structure of the object being imaged. This is just another case where Artificial Intelligence (AI) outperforms human. To the best knowledge of the author, this thesis is not only a success but also the first attempt to reconstruct an OCT image at a pixel level. To even give a try on this kind of task, it would require fully annotated OCT images and a lot of them (hundreds or even thousands). This is clearly impossible without a powerful simulation tool like the one developed in this thesis.