Rice University Research Repository


The Rice Research Repository (R-3) provides access to research produced at Rice University, including theses and dissertations, journal articles, research center publications, datasets, and academic journals. Managed by Fondren Library, R-3 is indexed by Google and Google Scholar, follows best practices for preservation, and provides DOIs to facilitate citation. Woodson Research Center collections, including Rice Images and Documents and the Task Force on Slavery, Segregation, and Racial Injustice, have moved here.



 

Recent Submissions

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Trapped-ion quantum simulation of electron transfer models with tunable dissipation
(AAAS, 2024) So, Visal; Duraisamy Suganthi, Midhuna; Menon, Abhishek; Zhu, Mingjian; Zhuravel, Roman; Pu, Han; Wolynes, Peter G.; Onuchic, José N.; Pagano, Guido; Center for Theoretical Biological Physics
Electron transfer is at the heart of many fundamental physical, chemical, and biochemical processes essential for life. The exact simulation of these reactions is often hindered by the large number of degrees of freedom and by the essential role of quantum effects. Here, we experimentally simulate a paradigmatic model of molecular electron transfer using a multispecies trapped-ion crystal, where the donor-acceptor gap, the electronic and vibronic couplings, and the bath relaxation dynamics can all be controlled independently. By manipulating both the ground-state and optical qubits, we observe the real-time dynamics of the spin excitation, measuring the transfer rate in several regimes of adiabaticity and relaxation dynamics. Our results provide a testing ground for increasingly rich models of molecular excitation transfer processes that are relevant for molecular electronics and light-harvesting systems.
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Impact of Surface Enhanced Raman Spectroscopy in Catalysis
(American Chemical Society, 2024) Stefancu, Andrei; Aizpurua, Javier; Alessandri, Ivano; Bald, Ilko; Baumberg, Jeremy J.; Besteiro, Lucas V.; Christopher, Phillip; Correa-Duarte, Miguel; de Nijs, Bart; Demetriadou, Angela; Frontiera, Renee R.; Fukushima, Tomohiro; Halas, Naomi J.; Jain, Prashant K.; Kim, Zee Hwan; Kurouski, Dmitry; Lange, Holger; Li, Jian-Feng; Liz-Marzán, Luis M.; Lucas, Ivan T.; Meixner, Alfred J.; Murakoshi, Kei; Nordlander, Peter; Peveler, William J.; Quesada-Cabrera, Raul; Ringe, Emilie; Schatz, George C.; Schlücker, Sebastian; Schultz, Zachary D.; Tan, Emily Xi; Tian, Zhong-Qun; Wang, Lingzhi; Weckhuysen, Bert M.; Xie, Wei; Ling, Xing Yi; Zhang, Jinlong; Zhao, Zhigang; Zhou, Ru-Yu; Cortés, Emiliano
Catalysis stands as an indispensable cornerstone of modern society, underpinning the production of over 80% of manufactured goods and driving over 90% of industrial chemical processes. As the demand for more efficient and sustainable processes grows, better catalysts are needed. Understanding the working principles of catalysts is key, and over the last 50 years, surface-enhanced Raman Spectroscopy (SERS) has become essential. Discovered in 1974, SERS has evolved into a mature and powerful analytical tool, transforming the way in which we detect molecules across disciplines. In catalysis, SERS has enabled insights into dynamic surface phenomena, facilitating the monitoring of the catalyst structure, adsorbate interactions, and reaction kinetics at very high spatial and temporal resolutions. This review explores the achievements as well as the future potential of SERS in the field of catalysis and energy conversion, thereby highlighting its role in advancing these critical areas of research.
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Validation studies of the FLASH-TV system to passively measure children’s TV viewing
(Springer Nature, 2024) Vadathya, Anil Kumar; Garza, Tatyana; Alam, Uzair; Ho, Alex; Musaad, Salma M. A.; Beltran, Alicia; Moreno, Jennette P.; Baranowski, Tom; Haidar, Nimah; Hughes, Sheryl O.; Mendoza, Jason A.; Veeraraghavan, Ashok; Young, Joseph; Sano, Akane; O’Connor, Teresia M.
TV viewing is associated with health risks, but existing measures of TV viewing are imprecise due to relying on self-report. We developed the Family Level Assessment of Screen use in the Home (FLASH)-TV, a machine learning pipeline with state-of-the-art computer vision methods to measure children’s TV viewing. In three studies, lab pilot (n = 10), lab validation (n = 30), and home validation (n = 20), we tested the validity of FLASH-TV 3.0 in task-based protocols which included video observations of children for 60 min. To establish a gold-standard to compare FLASH-TV output, the videos were labeled by trained staff at 5-second epochs for whenever the child watched TV. For the combined sample with valid data (n = 59), FLASH-TV 3.0 provided a mean 85% (SD 8%) accuracy, 80% (SD 17%) sensitivity, 86% (SD 8%) specificity, and 0.71 (SD 0.15) kappa, compared to gold-standard. The mean intra-class correlation (ICC) of child’s TV viewing durations of FLASH-TV 3.0 to gold-standard was 0.86. Overall, FLASH-TV 3.0 correlated well with the gold standard across a diverse sample of children, but with higher variability among Black children than others. FLASH-TV provides a tool to estimate children’s TV viewing and increase the precision of research on TV viewing’s impact on children’s health.
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Thermal imaging through hot emissive windows
(Springer Nature, 2024) Prasad, Ciril Samuel; Everitt, Henry O.; Naik, Gururaj V.
It is not currently possible for an infrared camera to see through a hot window. The window’s own blinding thermal emission prevents objects on the other side from being imaged. Here, we demonstrate a path to overcoming this challenge by coating a hot window with an asymmetrically emitting infrared metasurface whose specially engineered imaginary index of refraction produces an asymmetric spatial distribution of absorption losses in its constituent nanoscale resonators. Operating at 873 K, this metasurface-coated window suppresses thermal emission towards the camera while being sufficiently transparent for thermal imaging, doubling the thermal imaging contrast when compared to a control window at the same temperature
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Distilling the knowledge from large-language model for health event prediction
(Springer Nature, 2024) Ding, Sirui; Ye, Jiancheng; Hu, Xia; Zou, Na
Health event prediction is empowered by the rapid and wide application of electronic health records (EHR). In the Intensive Care Unit (ICU), precisely predicting the health related events in advance is essential for providing treatment and intervention to improve the patients outcomes. EHR is a kind of multi-modal data containing clinical text, time series, structured data, etc. Most health event prediction works focus on a single modality, e.g., text or tabular EHR. How to effectively learn from the multi-modal EHR for health event prediction remains a challenge. Inspired by the strong capability in text processing of large language model (LLM), we propose the framework CKLE for health event prediction by distilling the knowledge from LLM and learning from multi-modal EHR. There are two challenges of applying LLM in the health event prediction, the first one is most LLM can only handle text data rather than other modalities, e.g., structured data. The second challenge is the privacy issue of health applications requires the LLM to be locally deployed, which may be limited by the computational resource. CKLE solves the challenges of LLM scalability and portability in the healthcare domain by distilling the cross-modality knowledge from LLM into the health event predictive model. To fully take advantage of the strong power of LLM, the raw clinical text is refined and augmented with prompt learning. The embedding of clinical text are generated by LLM. To effectively distill the knowledge of LLM into the predictive model, we design a cross-modality knowledge distillation (KD) method. A specially designed training objective will be used for the KD process with the consideration of multiple modality and patient similarity. The KD loss function consists of two parts. The first one is cross-modality contrastive loss function, which models the correlation of different modalities from the same patient. The second one is patient similarity learning loss function to model the correlations between similar patients. The cross-modality knowledge distillation can distill the rich information in clinical text and the knowledge of LLM into the predictive model on structured EHR data. To demonstrate the effectiveness of CKLE, we evaluate CKLE on two health event prediction tasks in the field of cardiology, heart failure prediction and hypertension prediction. We select the 7125 patients from MIMIC-III dataset and split them into train/validation/test sets. We can achieve a maximum 4.48% improvement in accuracy compared to state-of-the-art predictive model designed for health event prediction. The results demonstrate CKLE can surpass the baseline prediction models significantly on both normal and limited label settings. We also conduct the case study on cardiology disease analysis in the heart failure and hypertension prediction. Through the feature importance calculation, we analyse the salient features related to the cardiology disease which corresponds to the medical domain knowledge. The superior performance and interpretability of CKLE pave a promising way to leverage the power and knowledge of LLM in the health event prediction in real-world clinical settings.