Technology and form : iron construction and transformation of architectural ideals in nineteenth century France 1830-1889

이상헌 Massachusetts Institute of Technology (M.I.T.) 1996 해외박사

본 논문은 19세기 프랑스에서 철구조물의 발달과 함께 전개된 건축사상의 변천을 연구한다. 철구조물은 고전주의 형태규범과 근대적 과학기술이 모호 하게 공존하고 있었던 신고전주의 건축의 위기와 때를 같이하여 등장한다. 철은 건축의 기술적, 미학적 양측면에 영향을 미치면서, 이상주의자와 합리주 의자들에 의해 신고전주의 건축규범의 붕괴이후 새로운 건축양식을 창조할 상 징적 재료로서 떠올랐다. 그러나 철구조물 자체로서는 새로운 건축양식을 창 조할 수 없었으며, 고전주의 미학의 붕괴이후 철구조물에 부여할 만한 절대적 미적규범도 존재하지 않았다. 이것은 근대건축의 근본적인 고민이었으며 부 르조아 문화의 본질적 모순이기도 했다. 19세기 중반의 구조적 합리주의자들은 재료 및 구조의 합리성 그리고 이성 에 바탕을 둔 새로운 근대건축양식을 창조하고자 했다. 그러나 이것이 불가 능하다는 것은 19세기말 아르누보 건축가들에 의한 철건축의 실험이 개인주의 적환상에 대한 열정으로 귀착되면서 입증되었다. 곧이어 합리주의자들은 건 축적 근대성의 상징적 재료를 철에서 철조콘크리트로 바꾸었으며, 이는 결국 기술과 형태 사이에는 근본적인 간격이 있다는 것, 즉 부르조와 합리주의의 근본적 고민을 다시 한번 확인시켜준 셈이다. 20세기초 근대주의 역사가들은 진화론적 근대 건축사를 기술하면서 합리주 의적 구조주의적 전통에 바탕을 두고 19세기 철건축을 20세기 근대건축의 맹 아로 간주하였다. 그러나 그들이 재발견한 철건축은 19세기에 받아들여지던 철건축이 아니라 20세기 근대미학에 의해 창조된 것이었다. 이러한 배후에는 20세기 건축사가 및 이론가들에 의한 재료와 구조의 미학화, 그리고 건축의 주관화가 있었던 것이다. 결론적으로 본 논문은 이러한 철건축에 대한 건축적 언설이 객관적 구조물 에서 주관적미학으로 전이되는 과정을 분석한다. 이러한 변천은 현대과학기 술의 발전과의 연관속에서 현대건축의 형성과정 및 현대건축의 사회내 존재양 식을 특징 짓는다. This dissertation investigates the transformation of architectural ideals brought about by the development of iron construction during the nineteenth century in France. �Ihe emergence of iron construction paralleled the crisis of neoclassicism, in which an ambiguous compromise between classical formal norms and modem science and technology was already manifest in the iron reinforced lintel. In the crisis of neoclassicism, iron, with its impact on both technological and formal aspects of architecture, emerged as a symbolic material to create a new style of modem architecture among utopians and rationalists. However, iron construction could not create a new style on its own; nor did there exist absolute formal aesthetic principles to impose on the construction. This is a fundamental dilemma of modem architectnre, an inherent contradiction of bourgeois culture. Stmctnral rationalists during the second half of the nineteenth century tried to resolve this contradiction by attempting to create a new style of architecture based on material and constructional rationality and reason. However, their inability to create a new style was finally proved in the last decade of the nineteenth century when the Art Nouveau exploitation of iron became a passion for individual fantasies, while engineers declared the triumph of their iron construction. Subsequent rationalists change of the material signifier of architectural modernity from iron to reinforced concrete testified to the fundamental gap between technology and form, and the dilemma of bourgeois rationalism. Early twentieth century modernist historians rediscovered nineteenth century iron construction as a precursor of modem architecture, constructing an evolutionary history of modem architecture based on the rationalist constructive tradition, from iron construction to reinforced concrete architecture. However, their Discovery of iron construction was purely an aesthetic invention of the twentieth century based on modernist avant garde aesthetics. Behind their apparent reconciliation of modem technology and architecture lay the aestheticization of material and construction, and the subjectivization of architecture. Thus, this dissertation analyzes the displacement of architectural discourses on iron construction from an objective construction to a subjective aesthetics. This shift characterized the further development of modem architecture and its mode of existence in modem society in relation to the development of modem technology.

Spectroscopy of Topological Materials and Their Technological Applications

Nguyen, Thanh Massachusetts Institute of Technology ProQuest Dis 2024 해외박사(DDOD)

Topological materials have been at the forefront of condensed matter physics and material science research in the past decade. The concept of topology has greatly shaped our modern understanding of matter including how band topology can tailor the exotic electronic and structural properties of a material. The prediction and subsequent experimental observation of topological semimetals stimulated an exploration of a substantial number of new materials. These range from verifying new exciting theoretical predictions to examining compilations of vast databases obtained through high-throughput computational and machine-learning approaches. In this thesis, we explore these new research directions by using new spectroscopic techniques to probe topology and other phonon-related phenomena in materials and by investigating potential applications of topological materials in modern-day technologies.We begin the dissertation by illustrating a chronicle of research into topology in condensed matter physics over the decades, the techniques of x-ray and neutron spectroscopies, and an overview of current progress in modern technologies. The first part of the dissertation describes using x-ray and neutron spectroscopies as a novel probe of topology. In particular, we describe how the nesting of Weyl nodes may lead to exotic phenomena such as anomalies in phonon dispersions and the stabilization of exotic magnetic orders, both of which are measurable using x-ray and neutron probes. We then focus on probing phonon transport in materials using x-ray scattering along two interesting directions. We demonstrate a method that overcomes the difficult task of measuring thermal properties of a thin film on a substrate. By measuring the gradual diffusive lattice relaxation subsequent to laser-induced heat using ultrafast x-ray diffraction, we are able to extract thermal conductivity of a thin film as well as the thermal boundary conductance between the film-substrate interface with lateral sensitivity and with information about local defects. Second, we probe a nonequilibrium state of matter termed many-body localization by demonstrating a departure from thermalization of phonons in disordered semiconductor superlattices.The second part of the dissertation focuses on technological applications of topological materials following their experimental discovery and recent theoretical developments. Topological materials may possess unique material properties resulting in applicability and possible large-scale implementation into certain technological niches based on current necessities. We investigate how topological materials may be part of future thermometric material candidates as they can acquire large entropy through unique Landau level quantization and large Berry curvature-induced anomalous Nernst effect. We describe measurements of nonlinear Hall effects in these materials which highlight their possible use in energy harvesting and terahertz applications. We also describe ideas of using topological materials as future post-copper back-end-of-the-line interconnects in integrated circuits due to favorable resistivity scaling with decreasing size. Finally, we showcase some research into spintronic-related logic and memory applications due to the generation of large spin-orbit-torques in these materials resulting in efficient current-driven switching capabilities.We conclude by presenting an outlook of future research directions in the field of topological materials and the prospects of their integration into modern-day electronic and thermal technologies.

Modeling Sustainable Mineral Supply Pathways to Meet Clean Energy Demand

Bhuwalka, Karan Massachusetts Institute of Technology ProQuest Dis 2024 해외박사(DDOD)

The adoption of renewable energy technologies hinges on the availability of many critical minerals. To meet the large demand for critical minerals, it is vital to scale up mineral supply in an environmentally and socially responsible way while maintaining low materials costs for key technologies. To guide policy and technology innovation that meets this objective, we need robust approaches for evaluating the availability and costs of materials. However, traditional approaches for assessing material availability or ‘criticality’ do not incorporate price feedback or a structural understanding of how material supply evolves. In this thesis, I build a model that simulates metal demand, mine opening and operation decisions, and mineral reserve development while incorporating price feedback. This model is used to evaluate how factors such as the rate of demand growth, materials substitutability and recycling rates impact materials prices and availability in the long term. The model is then applied to data on real mining projects for two key battery materials: nickel and lithium. Model simulations analyze supply pathways till 2040 to identify strategies that reduce the risk of materials supply constraints impacting clean energy technology deployment.Results demonstrate that a combination of high mining productivity, development of material substitutes and large recycling rates reduce the prevalence of availability risks from ~90% to just under 2% for materials experiencing high demand. In the nickel case, results show that environmental regulation can reduce impacts such as supply-chain emissions by 50% but lead to a 2x increase in nickel prices with only 70% of baseline nickel demand being satisfied. However, if regulations are combined with innovation that lowers processing costs and market coordination that reduces project development timelines and risks, over 90% of the demand is met without price increases. Similarly in the lithium case, reducing mine development timelines from 8 years to 6 years can increase the percentage of demand satisfied from 82% to 92% by moderating supply shortages and lithium prices.

Analytics and Decision Making in Sustainable Operations

Thayaparan, Leann Massachusetts Institute of Technology ProQuest Dis 2024 해외박사(DDOD)

Sustainable operations has transformed in the past decade, as interest from consumers, companies and regulators has increased. There has been a growing excitement and necessity to leverage the large-scale data collected to improve the modelling and decision making around sustainable operations. In this thesis, we introduce new methodologies to support data-driven sustainable operations, and in specific deal with topics around electric vehicles and the COVID-19 pandemic.In Chapter 2, we consider the problem of electric vehicles (EVs) as distributed storage for the electric grid. While electric vehicles can act as batteries supporting both the home and the electric grid, uncertainty around car usage must be accounted for before such models can be used in practice. We introduce a driver behavior-focused dynamic optimization for the charging and discharging of electric vehicles. We characterize policies that are interpretable to drivers to address distrust of automatized discharging of car batteries and prove analytically the regimes under which such policies are optimal. Finally, we work closely with an American EV manufacturer to show the dollar and carbon benefit that can be expected to be saved from discharging based on their driving behavior. We do this by clustering drivers based on their driving to derive probability distributions of when and how much drivers use their car to feed into the dynamic optimization.We further develop the challenge of data-driven decision making in sustainability through Chapter 3. Rather than learning probability distributions as in Chapter 2, we introduce a deterministic approach in which a tree-ensemble model, specifically a random forest, forecasts how much drivers use their EV. This gives rise to a challenge from the predict-then-optimize literature around the tractability of optimizations in which an objective function is determined by a tree ensemble model. In this chapter we introduce an Upper Bounding Method for Optimizing over Tree Ensemble Models, UMOTEM. We demonstrate the scalability of UMOTEM, showing it grows linearly with regard to both the number of trees in the ensemble as well as those trees' depth. This is a strong improvement over comparable formulations which grow exponentially. We also bound the optimality gap introduced through the approximation, characterizing it using features of the random forest such as leaf separation and in-sample error. We computationally compare our approximation to similar methods, demonstrating that the algorithm captures over 90% of optimality in 2% of the runtime for publicly available datasets. Finally, we demonstrate the use of UMOTEM through two case studies. First, we take the same case as Chapter 2, and show how UMOTEM can be leveraged to optimize the charging and discharging of EVs. Second, we work closely with Oracle Retail to apply UMOTEM to promotion scheduling in order to determine an optimally markdown strategy for a fashion retailer.In the final chapter of this thesis, we address data-driven decision making in one of the other major operational challenges to affect the globe, the COVID-19 pandemic. We develop a SIR-based model that can account for multiple waves. This model is agnostic to what drives the new waves (new variants, behavior changes, government policies, etc.) but takes a data-driven approach to identify when infection rates change. We prove analytical guarantees on how fast new waves can be detected.

The Structure and Behavior of Plate Boundary Regions Through the Wilson Cycle

Molitor, Zachary Massachusetts Institute of Technology ProQuest Dis 2024 해외박사(DDOD)

This thesis explores the geochemical and geophysical properties of plate boundary regions in the Atlantic, East Africa, the New England Appalachians, and subduction zones around the Pacific Ocean. Chapter One presents geochemical constraints on the extent of enriched mantle from upwelling mantle plumes relative to the observed extent of topographic swells related to mantle flow. It builds on this data by presenting a new geophysical model of mantle flow around mantle plumes that constrains the viscosity structure of the upper mantle and emphasizes the role of dynamic pressure from flowing mantle in the generation and maintenance of plume swell topography. Chapter 2 presents new experimental constraints on subduction zone melts at 2.4 GPa and temperatures representative of conditions near the top of the subduction slab in the mantle wedge. Our experimental constraints support existing hypotheses that proposed erupted primitive high magnesian andesites are produced through mantle melting and mixing of melts in the mantle wedge, while also presenting novel constraints on the concentration of water that can be maintained in glass during quenching. Chapter 3 presents a field-based study of low melt fraction migmatites in central New Hampshire. In it, we utilize a unique approach, based on the compaction length scale, to calculate the shear viscosity of the migmatite during deformation associated with the Acadian-Neo Acadian orogeny and the presence of an orogenic plateau. Chapter 4 presents a detailed macro- and microscale analysis of structures and deformation in southern New England related to contemporaneous strike-slip conjugate faulting in the upper crust. In it we present new electron backscatter diffraction (EBSD) data and in situ trace element and U-Pb isotopic compositions for monazite and titanite. These datasets provide quantitative constraints on the style and conditions of deformation in the weak middle crust beneath an orogenic strike-slip conjugate shear system (in the upper crust). Furthermore, this data constrains the late Paleozoic stress field in New England and the kinematics of collision between Gondwana and Laurasia.

Optical Property Prediction and Molecular Discovery Through Multi-Fidelity Deep Learning and Computational Chemistry

Greenman, Kevin P Massachusetts Institute of Technology ProQuest Dis 2024 해외박사(DDOD)

Optical properties are crucial for the design of molecules for numerous applications, including for display technologies and biological imaging. The accurate prediction of these properties has been the subject of decades of work in both physics-based approaches and statistical modeling. Recently, large datasets of both computed and experimental optical properties have become available, along with the advent of powerful deep learning approaches cable of learning meaningful representations from these large datasets. This thesis presents new approaches for predicting optical properties by fusing the experimental and computational data in multi-fidelity models that achieve greater accuracy and generalizability than previous methods. Additionally, it conducts a thorough benchmark of various strategies for handling multi-fidelity data to inform the modeling choices of future practitioners working with optical properties and beyond. Despite the greater availability of optical property data recently, the near-infrared (NIR) region of the spectrum remains more data-sparse despite its promise in many applications. This thesis demonstrates the shortcomings of existing methods for predicting optical properties in this region of chemical space and recommends best practices for future research in this area. Finally, this thesis highlights successful usage of data-driven optical property prediction for the discovery of novel molecules for specific applications.

First Explicit Reciprocity Law for Unitary Friedberg—Jacquet Periods

Zanarella, Murilo Corato Massachusetts Institute of Technology ProQuest Dis 2024 해외박사(DDOD)

In the early 2000's, Bertolini and Darmon introduced a new technique to bound Selmer groups of elliptic curves via level raising congruences. This was the first example of what is now termed a "bipartite Euler system", and over the last decade we have seen many breakthroughs on constructing such systems for other Galois representations, including settings such as twisted and cubic triple product, symmetric cube, and Rankin--Selberg, with applications to the Bloch--Kato conjecture and to Iwasawa theory.This thesis studies the case of Galois representations attached to automorphic representations on a totally definite unitary group U(2r)over a CM field which are distinguished by the subgroup U(r) x U(r). We prove a new ``first explicit reciprocity law'' in this setting, which has applications to the rank 0 case of the corresponding Bloch--Kato conjecture.

Essays on the Role of Identity in Economic and Political Behavior

Ruebeck, Hannah K Massachusetts Institute of Technology ProQuest Dis 2024 해외박사(DDOD)

These essays consider the role of various personal and social identities and resulting decision-making in the domains of education, work, and political participation. The first essay studies beliefs about experiencing racial or gender discrimination, or perceived discrimination, and its consequences for worker behavior. Using a large randomized controlled trial (RCT, N=5,000) in a constructed online labor market, I show that perceived racial and gender discrimination has large negative effects on worker retention, future labor supply, and cooperation with managers and that these effects are driven by large psychological costs to interacting with a biased manager. Firms can therefore improve both equity and efficiency by reducing perceived discrimination. I then test whether implementing hiring procedures that reduce the potential for actual discrimination are effective at reducing perceived discrimination. The procedures I test—blinding hiring managers to demographics and using unbiased algorithms—at best moderately reduce rates of perceived discrimination when members of minority groups remain highly under-represented.The second essay studies childhood confidence, a potential determinant of educational and labor-market behavior when ability is imperfectly observed. This essay documents two main facts in a large, national sample of children whose outcomes are followed for 20 years. First, childhood confidence in math and reading is starkly gendered along stereotypical lines: girls are more likely to be under-confident in math and over-confident in reading, and vice-versa for boys. Second, childhood over- and under-confidence in math strongly predicts adolescent test scores, educational attainment, and majoring or working in STEM.The final essay studies political efficacy, or beliefs about government responsiveness to citizen preferences and action in an RCT with 6,000 participants. In the context of US climate policy, we test how these beliefs and preferences for government action change when citizens learn about the recent, largest climate bill in US history. Learning about policy progress has small positive effects on political efficacy and small negative effects on preferences for the government to focus on climate policy. These countervailing effects may be why we see no effect of this treatment on citizen climate action. On the other hand, additionally watching a short, fictional narrative about a young, initially apathetic woman who goes on to organize a climate march has large effects on political efficacy and subsequently large effects on donations to climate lobbying groups and revealed interest in climate marches.

Defining Key Engineering Parameters to Advance Electrochemical CO₂ Separation Technologies

Clarke, Lauren E Massachusetts Institute of Technology ProQuest Dis 2024 해외박사(DDOD)

Carbon dioxide (CO₂) capture, coupled with utilization or storage, is anticipated to help facilitate society-wide decarbonization by reducing emissions of current thermochemical processes and addressing hard-to-decarbonize sectors. However, current carbon capture approaches remain energy intensive, expensive, and dependent on fossil-fuel derived heat, challenging sustainable, large-scale deployment. Electrochemical technologies for CO₂ separation have recently gained attention, as these methods have the potential to achieve higher efficiencies, directly utilize renewable energy, enable modular devices, and operate at (or near) ambient conditions. Various redox chemistries have been identified and experimentally tested for their ability to drive CO₂ separation; however, open questions remain about the expected performance of these systems and how these conceptual processes can be effectively designed at scale. Furthermore, there are several important and interdependent performance descriptors (e.g., energetic efficiency, faradaic efficiency, and separation capacity or flux) whose complex interplay convolutes determination of the optimal design space.To address this, my research focuses on developing modeling frameworks to better understand the relationship between material properties, operating conditions, and key system performance metrics within electrochemical CO₂ separation systems. First, I discuss a thermodynamic modeling analysis that explores tradeoffs between the upper bounds on energetic and faradaic efficiencies, and identify key molecular/system properties that balance these competing metrics. Then, I describe a cell-level model that was developed to evaluate the impact of several key variables on energetic penalties (on top of the thermodynamics) for liquid-fed electrochemical reactors (used in “4-stage” system configurations). I also demonstrate how this model can be used to explore pathways towards improving the system energetic efficiency. Finally, I describe a 2D model for gas-fed electrochemical cells (used in “2-stage” system configurations) and demonstrate how electrochemically-generated concentration gradients can induce natural convection that can significantly impact observed cell performance. The results from this model highlight how designing cells that harness natural convection can enhance achievable current densities and, ultimately, CO₂ separation fluxes. Overall, the findings from these collective modeling analyses inform ongoing molecular design and device engineering campaigns, provide insight into key system properties, and establish engineering guidelines for electrochemical CO₂ separation devices across scales.

Likelihood-Free Hypothesis Testing and Applications of the Energy Distance

Gerber, Patrik Robert Massachusetts Institute of Technology ProQuest Dis 2024 해외박사(DDOD)

This thesis studies questions in nonparametric testing and estimation that are inspired by machine learning. One of the main problems of our interest is likelihood-free hypothesis testing: given three samples X, Y and Z with sample sizes n, n and m respectively, one must decide whether the distribution of Z is closer to that of X or that of Y. We fully characterize the problem’s sample complexity for multiple distribution classes and with high probability. We uncover connections to two-sample, goodness-of-fit and robust testing, and show the existence of a trade-off of the form mn ≍ k/ε4, where k is an appropriate notion of complexity and ε is the total variation separation between the distributions of X and Y. We generalize our problem to allow Z to come from a mixture of the distributions of X and Y, and propose a kernel-based test for its solution, and also verify the existence of a trade-off between m and n on experimental data from particle physics. In addition, we demonstrate that the family of “classifier accuracy” tests are not only popular in practice but also provably near-optimal, recovering and simplifying a multitude of classical and recent results. Finally, we study affine classifiers as a tool for estimation and testing, with the key technical tool being a connection to the energy distance. In particular, we propose a density estimation routine based on minimizing the generalized energy distance, targeting smooth densities and Gaussian mixtures. We interpret our results in terms of half-space separability over these classes, and derive analogous results for discrete distributions. As a consequence we deduce that any two discrete distributions are well-separated by a half-space, provided their support is embedded as a packing of a high-dimensional unit ball. We also scrutinize two recent applications of the energy distance in the two-sample testing literature.