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A Radical Approach to Photochemical Transformations Using Earth-Abundant Metals
No full textThe continuing emergence of visible light-mediated photochemistry in modern organic syntheses has allowed facile access to powerful, unconventional reaction manifolds to synthesize diverse small molecules. Conventional photocatalysis/photoredox heavily relies on noble-metal based, coordinatively-saturated mononuclear photoactive complexes to perform a bimolecular outer-sphere single electron transfer (OSET) process for the generation of open-shell radical species. While powerful, the efficiency of this approach is limited by the bimolecular diffusion rate of reactants and photocatalysts and a redox-potential matching requirement for productive oxidative/reductive quenching. By contrast, direct coordination of a metal and substrate can offer a complementary reaction manifold by facilitating inner-sphere single electron transfer (ISET) to promote the homolytic cleavage of this metal-ligand bond and generate an open-shell radical, which can bypass the OSET redox-potential matching prerequisite. Light-induced ligand-to-metal charge transfer (LMCT) is such reaction manifold, allowing for selective single electron oxidation of the coordinated ‘ligand’. In general, this reaction scheme can convert anionic ligands to the corresponding radical forms which can function in various radical transformations including functionalization of unsaturated hydrocarbons and intermolecular C-H functionalization. Most importantly, a great synthetic advantage of this reaction manifold is that these processes are found in many early transition-metal (3d metals), which are significantly more earth-abundant than noble metals (e.g. Ir, Ru) used in traditional photoredox catalysts, presenting a low cost and sustainable alternative to noble metal photocatalysis. Apart from our exploration in LMCT catalysis, we also demonstrated radical ligand transfer (RLT) as an effective pathway to sequester transient alkyl radical species, introducing a powerful tool to utilize these reactive species for enhancing molecular complexity of feedstock chemicals. Herein, I will share my research of radical photochemical transformations enabled by earth-abundant metals and we hope our study of earth-abundant metal photocatalysis can inspire chemists to design sustainable pathways in pharmaceuticals and natural product syntheses.
Radical difunctionalization is a powerful reaction scheme to incorporate useful functionalities onto unsaturated hydrocarbons, especially the prevalent unactivated alkene class. While atom transfer radical addition (ATRA) has been adopted in difunctionalization of unactivated alkenes to perform haloalkylation using the halide from the alkyl halide reagents, a more versatile reaction scheme that allows the incorporation of other functionalities by leveraging in situ generated transient alkyl radical intermediates is desirable. In chapter 1, we proposed bio-inspired radical ligand transfer (RLT) for taming transient alkyl radical species generated from radical addition to unactivated alkenes. Learning from the radical rebound process of the cytochrome P450 enzyme and non-heme iron-dependent oxygenases, we developed RLT catalysis to incorporate diverse functionalities to minimally functionalized alkenes. This efficient ligand transfer process outcompetes unproductive ATRA, indicating a powerful reaction manifold for functionalizing transient alkyl radical species. The RLT chemistry also inspires us to explore other alkene difunctionalization using earth-abundant metals.
Vicinal diamine motifs are prevalent in bioactive molecules, pharmaceuticals, and molecular catalysts, underscoring their significance, and olefin diazidation has emerged as a promising strategy for synthesizing these motifs. Although synthetic precedents have utilized highly oxidative azidobenziodoxolone (ABX, Zhdankin reagent) or electrochemical methods to prepare this privileged motif, these protocols were often confined to limited substrate scope and procedural complexity. Motivated by our development of radical ligand transfer (RLT), we introduced the photochemical diazidation enabled by ligand-to-metal charge transfer (LMCT) and radical ligand transfer (RLT) in chapter 2. Leveraging the merger of these two reaction manifolds, we utilize a stable, earth-abundant, and inexpensive iron salt to function as both radical initiator (LMCT) and terminator (RLT) to synthesize valuable diazidated products. Mechanistic understanding of this cooperative LMCT/RLT also motivated us to develop a photocatalytic diazidation protocol and further expand this chemistry to photocatalytic dichlorination and regioselective fluorochlorination, suggesting the versatility of this tandem scheme. This cooperative scheme can also be applied to photocatalytic decarboxylative C-N bond formation, further demonstrating diverse nucleophilic reactants can be utilized in open-shell radical generation via LMCT, which can subsequently participate in cooperating reaction pathways. This cooperative system prompted us to explore sustainable photocatalysis, with goals of eliminating the usage of exogenous oxidants/reductant, driven by the cooperation of LMCT and other pathways.
The introduction of fluoroalkyl groups to parent molecules is a powerful tool to modulate biological and physiological activities of these compounds through enhancement of lipophilicity, bioavailability and metabolic stability. One direct way to introduce these fluoroalkyl groups is through hydrofluoroalkylation of alkenes. Early studies have explored the utilization of expensive and/or oxidative fluoroalkylating reagents and precious metals, which significantly restrict the application of these strategies. In many respects, fluoroalkyl carboxylic acids are the most ideal fluoroalkylating source due to low cost and availability, with trifluoroacetic acid (TFA, $9/mol) as an example representing a desirable CF3 source for hydrotrifluoromethylation. However, the decarboxylation of TFA (and other fluorocarboxylic acids) is known to be extremely challenging due to its high oxidation potential, with previous approaches tentatively utilizing TFA confined to pre-installation of redox moieties to overcome this barrier, drastically decreasing the atom/step economy in these methods. In chapter 3, we show how leveraging the synthetic advantage of LMCT, which can override OSET redox-potential mismatching, allows us to develop a photocatalytic hydrofluoroalkylation protocol using fluoroalkyl carboxylic acids including TFA and other feedstock fluorocarboxylic acids enabled by cooperative LMCT and hydrogen atom transfer (HAT). Critical to the success is the cooperation of earth-abundant iron LMCT and redox-active thiol HAT, offering a mild and redox-neutral protocol to synthesize fluorine-containing molecules without the preactivation of feedstock fluoroalkyl acids. Following the development of photocatalytic hydrofluoroalkylation, we took inspiration from our development of photocatalytic diazidation and dichlorination, reasoning these (pseudo)halide X-type ligands could be equally applied in an analogous hydrofunctionalization reaction manifold. Exciting, we found this to be true and further expand cooperative LMCT/HAT to achieve a photocatalytic anti-Markovnikov hydrochlorination of unsaturated hydrocarbons. Enabled by selective oxidation of anionic chloride using weakly oxidizing iron to promote LMCT reactivities, previous challenging substrates are tolerated in our protocol, giving high anti-Markovnikov regioselectivity. Moreover, this hydrochlorination strategy can be applied to diverse alkynes, offering facile routes to preparing alkenyl chlorides in high regioselectivities and good stereoselectivities. Additionally, with simple adjustment of deuterated co-solvent, both deuterochlorination of alkenes and alkynes behave well in our redox-neutral system, providing another strategy of isotopologue syntheses. Lastly, this cooperative LMCT/HAT also inspires us to develop photocatalytic hydroazidation where we observe a critical ligand-acceleration effect. The facile photochemical generation of azidyl radical also allows us to explore LMCT in combination with halogen atom transfer (XAT) to develop regioselective haloazidation. These azidation protocols can address previous limitations in oxidative/corrosive reagent usage, high loading of metal sources, or limited substrate scope. Importantly, the cooperation of iron LMCT and thiol HAT has showcased a mild and general solution to hydrofunctionalization of unsaturated hydrocarbons.
In this thesis, I have investigated the photochemical transformations enabled by earth-abundant metals, exploring the process of radical ligand transfer (RLT), ligand-to-metal charge transfer (LMCT), hydrogen atom transfer (HAT) and most importantly, the cooperation of these reaction manifolds which allows diverse transformations, establishing earth-abundant metal (photo)catalysis as a competitive synthetic manifold in accessing molecules of high value. These studies have been enabled by increasing mechanistic understanding of each reaction, fueling our continuous efforts in earth-abundant metal catalysis. We hope these studies enabled by earth-abundant metals communicate the importance of promoting sustainable (photo)catalysis in synthetic chemistry and serve as a powerful tool to synthetic chemists. We expect sustainable metal photocatalysis will keep enabling exciting chemistry
Towards All-Optical Circuit-Switched Datacenter Network Architectures with Low Energy and High Performance
No full textSince the genesis of the “cloud”, network infrastructure has become ubiquitous across the globe and is expected to support highly customized, fine-grained applications (e.g., HPC, distributed ML, DNN, etc.) with stringent performance requirements. However, as we move to the “post-Moore’s law era” of networking, CMOS-based electrical packet-switch ASICs are struggling to cope up with the increasing capacity while maintaining low power consumption and cost. Moreover, environmental awareness makes green and long-term sustainable cloud infrastructure design an absolute necessity. This energy-critical situation has led to several recent proposals regarding all-optical circuit-switched network core design for sustainable future-generation clouds. Optical circuit-switching (OCS) technologies are the key components that make those proposals fundamentally promising, as OCS is inherently eco-friendly along with the unique advantages: a) agnostic to data rate, b) negligible/zero power consumption, c) negligible forwarding latency, and d) no need for a frequent upgrade. However, the existing OCS core-based cloud architectures pose several challenges such as a) lack of native multicast capability, b) inability to handle traffic skewness, and c) terrible tail performance of individual flows. Fundamentally, these challenges are inherent to the OCS properties and operational abstraction of existing OCS cores. First, OCS can only provide point-to-point circuits and hence cannot have multicast support. Second, round-robin OCS core architectures lack the freedom of path diversity, leading to poor performance under skewed traffic. Third, the flows incur subsequent disruption due to periodic OCS downtime, leading to unpredictable tail performance. In my thesis, I envision a holistic low-energy and high-performance cloud architecture, capable of addressing all three challenges. To address the first challenge, I propose Shufflecast: a separate optical core to support energy-efficient high-performance multicast, complementing the existing unicast-capable all-optical core. Shufflecast leverages small fanout, inexpensive, passive optical splitters to connect the Top-of-rack (ToR) switch ports, ensuring data-rate agnostic, low-power, physical-layer multicast. To address the second challenge, I propose OSSV: a combination of OCS-based core (between ToR switches) and OCS-based reconfigurable edge (between servers and ToR switches). While the OCS core is traffic agnostic and realizes reconfigurably non-blocking ToR-level connectivity, the OSSV edge reconfigures itself to rectify the incoming traffic skewness. Such spatial flexibility to reorganize the flows can largely compensate for the lack of core-level path diversity. To address the third challenge, I propose Phoenix: a more flexible OCS core and OCS edge-based architecture with precise space and time-domain control. Apart from the suitable locations, Phoenix edge can also find opportunistic moments for the flow reorganization that can minimize the OCS downtime-induced disruption. Overall, the highly flexible optical edge with both space and time-domain flexibility can significantly improve the tail performance of individual flows under realistic workloads. We extensively evaluate several aspects of these architectures with large-scale simulations and testbed implementation. We believe such holistic system design can make all-optical circuit-switched network cores widely acceptable and adoptable to the community
Probe Broadband Nonlinear Optical Properties of Quantum Materials
No full textThe past decade has witnessed a rapid growth of the field of quantum materials, including but not limited to strong correlated electron systems, topological materials, and two-dimensional van der Waals crystals. The reason why they have got so much research attention is not only because of their rich physics, but also because their novel properties can be sensitive to various external stimuli, enabling high degrees of freedom to manipulate their characteristics by our own desire. Among them, strong-field optical excitation holds promise to probe atomic structures of crystals and open a new pathway for engineering quantum materials by driving the solids into nonlinear or non-equilibrium regimes and realizing new metastable phases as well as ultrafast material control and manipulations.
This thesis first presents the observation of giant nonlinear optical second harmonic generation (SHG) emission from the chiral carbon nanotube (CNT) systems due to strong excitonic effects. The CNT ensemble is centimeter-scale films of densely packed and aligned single enantiomer (6,5)- chiral CNTs that exhibit micro-fabrication compatibility. Detailed analysis demonstrates that the SHG emission originates from the intrinsic chirality and inversion symmetry breaking of the atomic structure of chiral CNTs. The observed value of the dominant element of the second-order nonlinear optical susceptibility tensor reaches 1.5 × 103 pm/V at a pump wavelength of 1030 nm, corresponding to the lowest-energy excitonic resonance. Our calculations based on many-body theories correctly estimate the spectrum and magnitude of such excitonically enhanced optical nonlinearity. These results are promising for developing scalable chiral CNT electronics, nonlinear photonics and photonic quantum computing.
To extend the study nonlinear optical behaviors of quantum materials in Terahertz region, which potentially opens a new realm of quantum materials manipulation and biosensing, here, for the first time, we report that quantum paraelectric SrTiO3 enables broadband surface phonon–polaritonic devices in 7–13 THz. As proof of concept, polarization-independent field concentrators are designed and fabricated to locally enhance intense, multicycle THz pulses by a factor of 6 and increase the spectral intensity by over 90 times. The time-resolved electric field inside the concentrators is experimentally measured by THz-field-induced second harmonic generation. Illuminated by a table-top light source, the average field reaches 0.5 GV m−1 over a large volume resolvable by far-field optics. These results potentially enable scalable THz photonics with high breakdown fields made of various commercially available phonon–polariton crystals for studying driven phases in quantum materials and nonlinear molecular spectroscopy
The Relationship between Eye Movements and Audiovisual Time-to-Contact Estimates in Young and Old Adults
No full textIntroduction: This dissertation investigated how saccadic eye movements, aging, and central vision loss affect time-to-contact (TTC) estimates in a dynamic, traffic-related environment. The goal was to explore the role of eye movement behaviors and sensory integration across different adult populations, including younger adults, older adults, and individuals with age-related macular degeneration (AMD), in both visual and audiovisual contexts.
Method: Two experiments were conducted. In Experiment 1, participants completed TTC tasks in visual-only and audiovisual conditions; half of them were instructed not to make saccadic eye movements. Experiment 2 examined TTC estimates and eye movement behaviors in younger and older adults, as well as individuals with AMD and older adults with normal vision, in both visual and audiovisual modalities.
Results: In Experiment 1, individuals who made saccadic eye movements had significantly higher TTC estimates in the visual-only modality than in the audiovisual condition. However, individuals who did not make saccadic eye movements had similar TTC estimates in the visual-only and audiovisual modalities. In Experiment 2, older adults exhibited shorter fixation durations compared to younger adults, and their longer fixations led to greater underestimations of TTC, which resulted in safer judgments. Individuals with AMD had less stable eye movements than older adults with normal vision but they made more accurate TTC estimates in the audiovisual condition compared to the visual-only condition, indicating a reliance on auditory cues to compensate for visual deficits.
Conclusion: These findings reveal that eye movements affect TTC judgments when information is presented visually but not when both auditory and visual stimuli are presented. Aging and central vision loss alter the relationship between eye movements and TTC judgments. Older adults with normal vision and adults with central vision loss underestimated TTC when they made longer fixations, suggesting that the relationship between TTC estiamtes and eye movements changes as a result of age and visual abilities
Fast and Expressive Sketch Structured Transform for Efficient Inference
No full textLinear transformations using learned weights are fundamental components of deep learning models. Prior research has shown that dense weight matrices can often be compressed by decomposition, quantization, sparsification, or random parameter sharing without losing accuracy, suggesting the benefit of more efficient transformations. Among variants of weight matrices, structured ones have limitations in expressivity and quality-efficiency tradeoffs. Unstructured matrices are incompatible with modern hardware, leading to slower training and inference. To address these challenges, we propose Sketch Structured Transform (SS1), an expressive and hardware-efficient operator that reduces tensor multiplications and accelerates inference. SS1 leverages random parameter sharing in a block-structured manner, reducing computation while preserving the expressiveness of parameter sharing. We empirically show that SS1 achieves better quality-efficiency tradeoffs than competing variants. Our theoretical analysis also indicates that SS1 can be combined with quantization for further compression, and the experimental results confirm this. Additionally, pre-trained models can be projected using SS1 and finetuned for efficient deployment. Our experiments highlight various applications of the SS1, including (a) Training GPT2 and DLRM models from scratch for faster inference. (b) Finetuning
projected BERT models for 1.31× faster inference while maintaining GLUE scores.
(c) Proof of concept with Llama-3-8b, showing 1.11× faster wall clock inference
using projected SS1 layers without finetuning
Sustainable Plasmonic Photocatalysis
No full textThroughout my Ph.D., I focused on addressing three key challenges in sustainable plasmonic photocatalysis: developing earth-abundant catalyst materials, utilizing cost-effective light sources, and optimizing industrially significant reactions. In my first project, I developed an efficient earth-abundant antenna-reactor photocatalyst for ammonia decomposition. The iron-based photocatalyst achieved efficiencies comparable to noble metals, such as ruthenium, and maintained its performance for gram-scale hydrogen production under light-emitting diode illumination. This work underscores the potential for efficient, light-driven hydrogen production using earth-abundant metals, demonstrating that plasmonic photocatalysis can activate a thermally inactive transition metal with illumination. My second project targeted methane steam reforming, a process responsible for half of global hydrogen production but also a significant source of CO₂ emissions. I developed a photocatalyst with high reactivity, selectivity, and stability for steam methane reforming. By utilizing light as the energy source and fine-tuning the selectivity of the catalysis, we achieved zero CO₂ emissions for this reaction. In my third project, I explored how the role of plasmonic photocatalysis in boosting not only the reactivity and selectivity of catalysts but also their stability. A notable observation is that catalysts display varying stability profiles when used in photocatalysis compared to traditional thermocatalysis, with illumination producing higher-energy hot carriers that enhance stability. This discovery suggests new opportunities for employing earth-abundant metals previously regarded as unreactive or unstable, paving the way for more sustainable catalytic processes
Asian American Community Study: Political and Social Attitudes in the Greater Houston Area
No full textThe Asian American population has rapidly grown and diversified across the Greater Houston area over the last several decades. In Harris County, the most populous in the area, Asian Americans constituted nearly 10% of all voters in 2024. In Fort Bend County, they accounted for 20% of eligible voters. As part of the Asian American Community Study (AACS), a project of the Houston Population Research Center at the Kinder Institute for Urban Research, this brief explores partisan affiliation and political attitudes among Asian residents in the Greater Houston area. The brief uses data collected from November 2024 to February 2025 and is divided into two parts. The first part examines respondents' partisan affiliations and political ideology across specific Asian ethnicities and places of origin, along with common sociodemographic predictors of partisanship. The second part focuses on respondents’ positions on key issues in U.S. politics, such as abortion rights, gun control, and immigration
Structure-utilized, Adaptive, and Efficient ML-based Proportional-Fair Scheduling in MIMO Networks for Non-stationary Channels
No full textProportional Fair (PF) scheduling is widely used in multi-user MIMO systems to balance throughput and fairness. However, PF scheduling is an NP-hard problem, and hence, practical deployments approximate the optimal solution for lower latency at the cost of sub-optimal performance. More recently, machine learning (ML)-based approaches have demonstrated strong performance with low latency. However, ML-based methods typically assume stationary channel distributions, making them vulnerable to performance degradation under dynamic network conditions such as user mobility and location changes. In this work, I develop a new ML-based scheduling framework that adapts to non-stationary wireless conditions in real time. The framework adopts a Graph Neural Network (GNN)-based scheduler—which captures both user-specific metrics and inter-user interference patterns—enabling structurally sample-efficient learning that generalizes well across users and topologies. Complementing this, an adaptive control module called On-Demand and Online Learning (ODOL) detects distribution shifts and triggers fine-tuning using expert demonstrations. To further reduce adaptation latency, we introduce an efficient online data collection strategy guided by user mobility structure, which accelerates sample acquisition during online fine-tuning. Extensive evaluations using simulations and real-world channel traces demonstrate that the proposed method consistently maintains high spectral efficiency and fairness with rapid policy adaptation under evolving channel conditions, making it a practical solution for next-generation wireless networks
Toward Utilizing Analog Rydberg Atom Systems for Quantum Machine Learning
No full textQuantum computing has shown theoretical promise for accelerating machine learning, including both generative and discriminative tasks. Recent advances in analog Rydberg atom quantum computers have opened new opportunities for near-term quantum machine learning applications, due to uniquely reconfigurable qubit positions and global multi-qubit operations. In this work, we present two complementary contributions that leverage the unique properties of this technology. First, we propose ReCon, the first implementation of quantum generative adversarialnetworks (GANs) on analog Rydberg atom quantum computers, achieving a 33% improvement in
image generation quality (measured via Frechet Inception Distance) compared to state-of-the-art
superconducting-qubit techniques. Second, we introduce ResQ, a novel framework that optimizes the dynamics of Rydberg atom quantum computers for discriminative tasks, specifically neural ordinary differential equation (neural ODE) based residual neural network (ResNets) classifiers. ResQ demonstrates how analog quantum computing can be tailored to solve classification problems in machine learning, achieving
a 36% improvement over classical neural ODE classifiers. Together, these works highlight the versatility and scalability of analog Rydberg atom quantum computing for both generative and discriminative quantum machine learning, advancing the feasibility and versatility of near-term hardware implementations for QML tasks