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AI-powered Data Mining for the Development of Sustainable Concrete Materials
No full textData mining has become essential to contemporary industrial and scientific research, playing a pivotal role in uncovering insights from large-scale industrial datasets and literature collections. The sustainable transition of the concrete industry, a major contributor to global CO₂ emissions, demands both operational optimization and scientific innovation. This thesis presents comprehensive data mining frameworks for both industrial and literature source data to support the development of more sustainable concrete materials. Focusing on concrete manufacturing, we develop AI-powered methodologies tailored to real-world industrial data and complex scientific literature. For industrial data mining, we propose to incorporate interpretability and realistic engineering design scenarios to enhance the reliability of both predictive and prescriptive modeling of concrete mixes containing supplementary cementitious materials (SCMs). A domain-informed amortized Gaussian process and a shallow multi-layer perceptron (MLP) are shown to possess superior scientific consistency in predicting time-varied compressive strength, and time-invariant slump and air content properties, respectively. The explainable surrogate property models are applied in mix design optimization under a variety of realistic scenarios considering different engineering design requirements and SCM costs and densities. The importance of the comprehensive property constraint set is demonstrated in comparison against a baseline using only 28-day strength constraint which results in unreasonable property values. The necessity to differentiate realistic scenarios is also highlighted through the differences of optimized mixes and their production costs and climate impacts. Higher design strength, higher design slump, lower design air content, higher SCM density, and higher SCM unit cost can drive up the production costs. Though stratification patterns in the production costs of optimized mixes are observed across different scenarios, the mix-wise climate impacts are not clearly stratified, indicating that substantial emission reduction can be achieved without significantly increasing costs, regardless of the realistic scenarios. For literature mining, a novel method that finetunes lightweight large language models (LLMs) (pythia-2.8B) with multichoice instructions is developed. With the multifaceted linguistic complexity of communication within the domain rendering it infeasible to adopt the conventional named-entity-recognition approach, the new method successfully achieves great information inference accuracy in a time-, cost-, and computation-efficient manner, outperforming the GPT-3.5 in-context learning baseline by over 20%. A knowledge graph is constructed with the literature-mined data, offering insights to promote alternative material substitution strategies in concrete production as the current commercial SCMs are not comprehensively sustainable in the longer term. Statistical summary and temporal trend analyses are adopted to provide both static and dynamic insights into the research landscape. Although SCMs have remained a research hotspot, results revealed a systematic shift in recent studies from commercial SCMs to other materials. Geopolymer and fine aggregate studies have surged in the recent period, while clinker feedstock and filler studies have declined. A node similarity metric is modified to develop a model-free link prediction algorithm, enhanced with random graph perturbation for robustness and uncertainty quantification. Through link prediction, the currently underexplored lime-pozzolan cement application emerges as a potentially promising future research direction.S.M.S.M
Imprinto: Enhancing Infrared Inkjet Watermarking for Human and Machine Perception
No full textCHI ’25, Yokohama, JapanHybrid paper interfaces leverage augmented reality to combine the desired tangibility of paper documents with the affordances of interactive digital media. Typically, virtual content can be embedded through direct links (e.g., QR codes); however, this impacts the aesthetics of the paper print and limits the available visual content space. To address this problem, we present Imprinto, an infrared inkjet watermarking technique that allows for invisible content embeddings only by using off-the-shelf IR inks and a camera. Imprinto was established through a psychophysical experiment, studying how much IR ink can be used while remaining invisible to users regardless of background color. We demonstrate that we can detect invisible IR content through our machine learning pipeline, and we developed an authoring tool that optimizes the amount of IR ink on the color regions of an input document for machine and human detectability. Finally, we demonstrate several applications, including augmenting paper documents and objects
Toward Everyday Perceptual and Physiological Augmentation
No full textUIST Adjunct ’25, Busan, Republic of KoreaHuman senses are fundamental to how we interpret and interact with the world. Computing devices are increasingly coupled with the human sensory system through interfaces such as smart glasses, earbuds, and wristbands. This opens up opportunities to dynamically mediate, modify, and augment perceptual experiences and physiological processes through multisensory stimulation. These devices go beyond assistive technologies designed for individuals with sensory impairments (e.g., hearing aids) and are now available for everyday use. Applications range from enriching immersive entertainment experiences to supporting well-being through multisensory interventions.
The UIST community has been a key venue for introducing many proof-of-concept prototypes in multisensory stimulation. However, gaps remain in systematically understanding how such technologies can be designed, studied, and contextualized in long-term, everyday use. This workshop will examine barriers to transitioning prototypes from proof-of-concepts into systems for real-world use. The session will feature keynote talks, demo sessions, and an interactive device-swap activity where participants exchange and wear different devices during the afternoon session, and conclude with an open discussion to develop implementation frameworks
Prosody in Kichwa
No full textThis thesis investigates the prosodic system of Salasaka Kichwa, focusing on the interaction between pitch, morphosyntactic structure, and word order in both elicited and spontaneous speech. Based on data from ten native speakers of the Salasaka community, the study analyzes approximately 150 utterances using Praat and ToBI-style prosodic annotation. The findings reveal a consistent alignment between the nuclear pitch accent and the leftmost constituent of the verb phrase in neutral declarative sentences, supporting the hypothesis that Salasaka Kichwa exhibits a head-final syntactic structure. This default prosodic alignment is disrupted by the presence of focus-sensitive or interrogative morphemes such as -mi and -chu, which reliably attract the pitch peak regardless of their position in the clause. In ditransitive constructions, pitch prominence consistently targets the dative-marked argument. Accusative-marked objects also receive prominence, but only when modified; in such cases, it is typically the modifying adjective or contrastive element that bears the highest pitch. Overall, the study demonstrates that prosodic prominence in Salasaka Kichwa is not governed by syntactic structure alone. Instead, it emerges from a layered interaction between morphology, information structure, and pragmatic marking offering new insights into how prosody encodes grammatical and communicative functions in underdescribed head-final languages.S.M
Searching for Mixed Octahedral-Tetrahedral Interstitial Hydrogen Occupation in Pd-Ti Sublattices: A Computational Study
No full textWith hydrogen conversion and storage technologies promising a revolution in the energy industry if volumetric energy density is increased, the loading of hydrogen to high concentrations in metal lattices has become of special interest. Here we use Projector Augmented-wave density functional theory methods to search the Pd-Ti-H system for stable instances of mixed tetrahedral-octahedral site occupation. We compute the energies of 42 hydrides constructed from seven metal sublattices: Ni₃Ti-prototype Pd₃Ti, CdI₂-prototype PdTi₂, and FCC four-atom unit cells of Pd, Pd₃Ti, PdTi, PdTi₃, and Ti. Our results suggest that mixed octahedral-tetrahedral occupation is energetically unfavorable in most cases, but a Li₃Bi-prototype hydride may be stable within the Pd₁-ₓTiₓH₃ system.S.B
A Review of Pnictogenides for Next-Generation Anode Materials for Sodium-Ion Batteries
No full textWith the growing market of secondary batteries for electric vehicles (EVs) and grid-scale energy storage systems (ESS), driven by environmental challenges, the commercialization of sodium-ion batteries (SIBs) has emerged to address the high price of lithium resources used in lithium-ion batteries (LIBs). However, achieving competitive energy densities of SIBs to LIBs remains challenging due to the absence of high-capacity anodes in SIBs such as the group-14 elements, Si or Ge, which are highly abundant in LIBs. This review presents potential candidates in metal pnictogenides as promising anode materials for SIBs to overcome the energy density bottleneck. The sodium-ion storage mechanisms and electrochemical performance across various compositions and intrinsic physical and chemical properties of pnictogenide have been summarized. By correlating these properties, strategic frameworks for designing advanced anode materials for next-generation SIBs were suggested. The trade-off relation in pnictogenides between the high specific capacities and the failure mechanism due to large volume expansion has been considered in this paper to address the current issues. This review covers several emerging strategies focused on improving both high reversible capacity and cycle stability
System-level Design, Fabrication, and Optimization of Sorbent-based Atmospheric Water Harvesting Devices
No full textSorption-based atmospheric water harvesting (SAWH) has been demonstrated as a promising avenue to addressing the increasing problem of water scarcity, especially in arid inland regions where alternative technologies are limited. However, current sorbent materials are often limited in their applicability due to system integration and device design constraints. In this thesis, we present advancement of atmospheric water harvesting technologies in both the passive and active design space by leveraging a system-level approach to modelling and optimization of devices. First, we discuss SAWH device fundamentals in terms of heat, mass, and fluid transport, and identify key components which impact device performance for both passive (solar) and active (electrical/chemical) systems, as quantified by our proposed performance metrics. Next, we develop a coupled heat and mass transport model of a passive, solar-driven atmospheric water harvesting device and quantify the impact of system variables on device operation. We use this model to fabricate an optimal system that efficiently utilizes a hydrogel-salt composite sorbent for record passive water production in the Atacama Desert. Furthermore, we propose an underlying mechanism for observed system-level degradation of our hydrogel-salt composite and demonstrate successful lifetime elongation of the sorbent in SAWH operation. Additionally, we use our fundamental understanding of SAWH to design an active device for portable use. Highly compact, lightweight, and energy dense, this system operates independent of external environment conditions and produces more than 2 L/day of potable water. Finally, a generalized topology optimization approach is proposed for sorbent scaffolding structures to further improve system water output while reducing power consumption and packing of atmospheric water harvesting devices.Ph.D
Diversity-oriented synthesis encoded by deoxyoligonucleotides
No full textDiversity-oriented synthesis (DOS) is a powerful strategy to prepare molecules with underrepresented features in commercial screening collections, resulting in the elucidation of novel biological mechanisms. In parallel to the development of DOS, DNA-encoded libraries (DELs) have emerged as an effective, efficient screening strategy to identify protein binders. Despite recent advancements in this field, most DEL syntheses are limited by the presence of sensitive DNA-based constructs. Here, we describe the design, synthesis, and validation experiments performed for a 3.7 million-member DEL, generated using diverse skeleton architectures with varying exit vectors and derived from DOS, to achieve structural diversity beyond what is possible by varying appendages alone. We also show screening results for three diverse protein targets. We will make this DEL available to the academic scientific community to increase access to novel structural features and accelerate early-phase drug discovery
RoboGrammar: Graph Grammar for Terrain-Optimized Robot Design
No full textWe present RoboGrammar, a fully automated approach for generating optimized robot structures to traverse given terrains. In this framework, we represent each robot design as a graph, and use a graph grammar to express possible arrangements of physical robot assemblies. Each robot design can then be expressed as a sequence of grammar rules. Using only a small set of rules our grammar can describe hundreds of thousands of possible robot designs. The construction of the grammar limits the design space to designs that can be fabricated. For a given input terrain, the design space is searched to find the top performing robots and their corresponding controllers. We introduce Graph Heuristic Search - a novel method for efficient search of combinatorial design spaces. In Graph Heuristic Search, we explore the design space while simultaneously learning a function that maps incomplete designs (e.g., nodes in the combinatorial search tree) to the best performance values that can be achieved by expanding these incomplete designs. Graph Heuristic Search prioritizes exploration of the most promising branches of the design space. To test our method we optimize robots for a number of challenging and varied terrains. We demonstrate that RoboGrammar can successfully generate nontrivial robots that are optimized for a single terrain or a combination of terrains
Secondary Structure in Enzyme‐Inspired Polymer Catalysts Impacts Water Oxidation Efficiency
No full textProtein structure plays an essential role on their stability, functionality, and catalytic activity. In this work, the interplay between the β-sheet structure and its catalytic implications to the design of enzyme-inspired materials is investigated. Here, inspiration is drawn from the active sites and β-sheet rich structure of the highly efficient multicopper oxidase (MCO) to engineer a bio-inspired electrocatalyst for water oxidation utilizing the abundant metal, copper. Copper ions are coordinated to poly-histidine (polyCuHis), as they are in MCO active sites. The resultant polyCuHis material effectively promotes water oxidation with low overpotentials (0.15 V) in alkaline systems. This activity is due to the 3D structure of the poly-histidine backbone. By increasing the prevalence of β-sheet structure and decreasing the random coil nature of the polyCuHis secondary structures, this study is able to modulates the electrocatalytic activity of this material is modulated, shifting it toward water oxidation. These results highlight the crucial role of the local environment at catalytic sites for efficient, energy-relevant transformations. Moreover, this work highlights the importance of conformational structure in the design of scaffolds for high-performance electrocatalysts