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.
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Recent Submissions
Public advocates, private advisors: the autonomy, function, and influence of the President's Council of Advisors on Science and Technology
(Frontiers Media S.A., 2024) Evans, Kenneth M.; Matthews, Kirstin R.W.; Baker Institute Science and Technology Policy Program
US national expert advisory bodies related to science, technology, and innovation (STI) policy have a wide range of missions, governing structures, operational practices, cultures, and impact on federal policymaking. This paper offers an analytical framework for assessing the autonomy, function, and influence of the President’s Council of Advisors on Science and Technology (PCAST), a federal advisory committee consisting of 30 elite scientists, engineers, and industry leaders appointed by and advising the president. We demonstrate that PCAST carries both a strong instrumental advisory role, providing substantive advice to White House STI policy development, and a significant symbolic advisory role, offering visible public support to presidential decisions and initiatives related to STI. However, we find that the council’s engagement with either or both roles has shifted depending on its available resources, the policy agenda of the administration it serves, the level of presidential attention, and the priorities of council leadership. The paper concludes with recommendations to guide future PCASTs in fulfilling their mission and appropriately influencing US national STI policy.
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.
Lateral Flow-Based Skin Patch for Rapid Detection of Protein Biomarkers in Human Dermal Interstitial Fluid
(American Chemical Society, 2024) Wilkirson, Elizabeth C.; Li, Danika; Lillehoj, Peter B.
Rapid diagnostic tests (RDTs) offer valuable diagnostic information in a quick, easy-to-use and low-cost format. While RDTs are one of the most commonly used tools for in vitro diagnostic testing, they require the collection of a blood sample, which is painful, poses risks of infection and can lead to complications. We introduce a blood-free point-of-care diagnostic test for the rapid detection of protein biomarkers in dermal interstitial fluid (ISF). This device consists of a lateral flow immunochromatographic assay (LFIA) integrated within a microfluidic skin patch. ISF is collected from the skin using a microneedle array and vacuum-assisted extraction system integrated in the patch, and transported through the lateral flow strip via surface tension. Using this skin patch platform, we demonstrate in situ detection of anti-tetanus toxoid IgG and SARS-CoV-2 neutralizing antibodies, which could be accurately detected in human ISF in <20 min. We envision that this device can be readily modified to detect other protein biomarkers in dermal ISF, making it a promising tool for rapid diagnostic testing.
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.
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.