Energy crisis and climate change are the imminent challenges faced by mankind that demand instant solutions in replacing fossil fuels with alternative clean energy sources. To meet this demand, the hydrogen fuel cell industry has witnessed tremendous growth within the past decade. However, the broad dissemination of proton-exchange membrane fuel cells (PEMFCs) is still limited by the high cost originated from the high loading of platinum-group metals (PGM) based catalysts to accelerate the sluggish oxygen reduction reaction (ORR) at the cathode. Therefore, it is central to design high-performance ORR catalysts and validate their performance in the membrane electrode assembly (MEA). In the first chapter of my dissertation, by combining theoretical modeling and experimental observations, we developed a binary experimental descriptor (BED) that directly correlates with the calculated oxygen binding energy ∆E O on Pt-alloy catalyst surface. The BED captures both the strain and Pt-transition metal coupling contributions based on experimental parameters extracted from X-ray absorption spectroscopy. This leads to an experimentally validated Sabatier plot wherein the BED can be used to predict not only the catalytic activity but also the stability of a wide range of Pt-alloy ORR catalysts. Based on the BED, we further designed an ORR catalyst wherein high activity and stability are simultaneously achieved. The second chapter is an extension of the first chapter, in which I demonstrated that tetrahedral PtCuNi catalysts, as an efficient multifunctional catalyst, did not only showed excellent ORR performance but also exhibited high methanol/ethanol oxidation reaction (MOR/EOR) performance, which can be potentially used in the direct methanol/ethanol fuel cells (DMFCs/DEFCs). By tailoring the surface composition, the optimal catalyst with a composition of Pt56Cu28Ni16 showed a MOR and EOR specific activity (SA) of 14.0  1.0 mA/cm2 and 11.2  1.0 mA/cm2, respectively; and mass activity (MA) of 7.0  0.5A/mgPt and 5.6  0.6 A/mgPt for the MOR and EOR, respectively. In the third chapter, I applied some highly promising ORR catalysts in MEA. In specific, I developed an ultralow Pt loading (total loading of 0.072 mgPt/cm2) and high-performance MEA using ultrathin platinum-cobalt nanowires (PtCoNWs) as cathode catalysts. The PtCoNWs showed a high ECSA of 73.2 m2/gPt and achieved an unprecedented MA of 1.06 � 0.14 A/mgPt [0.9 ViR-free] at the beginning of life (BOL) stage in MEA. This MA is 3.3 times that of the commercial Pt/C (0.32 A/mgPt) and far surpasses the Department of Energy (DOE) 2020 target (0.44 A/mgPGM). The PtCoNWs reached a peak power density of 1016 mW/cm2, outperforming the PtNWs (830 mW/cm2) and Pt/C (773 mW/cm2) with comparable Pt loading. After the AST, the PtCoNWs showed a respectable end of life (EOL) MA of 0.45 A/mgPt, remaining above the DOE 2020 BOL target. In the last chapter, I tried to tailor the interfacial properties to further enhance the surface microkinetic. In brief, I developed a facile and controllable molecular surface modification approach using dimethylformamide (DMF) to successfully improve the ORR performance of Pt-based catalysts. Significantly, our molecular dynamics (MD) simulations elucidated that DMF can disrupt interfacial water hydrogen-bonding networks, therefore allowing accelerated water exchange kinetics, facile O2 transport towards Pt surface, increased interfacial oxygen concentration, and adsorption time (around twice compared to pure Pt(111) surface), justifying enhanced ORR activity. We further applied this approach to a model Pt-alloy catalysts (PtCuNi), which achieved an unprecedented SA of 21.8 � 2.1 mA/cm2 at 0.9 V versus the reversible hydrogen electrode (RHE), about 2.65 times improvement comparing to original PtCuNi catalysts, and nearly double previously reported the best value, leading to an ultrahigh MA of 10.7 � 1.1 A/mgPt. Importantly, after 20,000 cycles of accelerated degradation tests (ADT), surface-modified PtCuNi showed even better SA and MA than the initial performance of original PtCuNi, suggesting the surface modification can also considerably extend the lifetime of the catalyst.

The ability to analyze gene expression data has had a fundamental impact in the biological sciences and on our understanding of the causes and mechanisms of disease. However, a significant statistical challenge is posed by the combination of the small number of replicates together with the large number of genes leading to an undesirable level of misclassified genes when identifying genes with differential expression levels. When multiple gene expression data sets are generated under the same set of experimental conditions, the ques- tion arises as to how to efficiently combine this information. Several methods in the literature have been suggested to aggregate ranked data from multiple sources. We introduce a new classifier, underpinned by Bayesian principles, called Peer Reinforced Ranker (PR-Ranker) which uses density estimation to approximate the probability that a gene is differentially expressed given a collection of ranked lists.

Our classifier is amenable to theoretical analysis when the number of genes and lists is large using the theory of large deviations. Under modest technical assumptions we show that asymptotically PR-Ranker has the smallest loss of any rank aggregation procedure. Moreover, we prove that other more ad hoc methods, such as Borda, have a strictly higher asymptotic rate of loss.

While the theoretical results are asymptotic, we perform a series of simulation studies that demonstrate that our classifier outperforms existing methods on datasets of realistic size for biological data. Furthermore, we show that the outperformance is even greater when the lists exhibit varying levels of noise or when some sources are corrupted. PR-Ranker automatically adapts to varying data quality and efficiently combines the data from different sources. Finally we apply PR-Ranker to a gene expression data set in a preeclampsia study. The top ranked genes identified were known to be biologically relevant to preeclampsia and our method achieved a substantially higher Consistency Index than other rank aggregation procedures.

Eukaryotes and prokaryotes contain neutral lipids in subcellular droplets as food reserves and/or for other purposes. Among all of the lipid droplets in diverse organisms, those in plant seeds are the most prominent. Lipid droplets in higher plants are present in seeds, pollens, fruits and flowers. Lipids from plants are important for human and farm-animal nutrition as well as industrial uses. Plant storage lipid droplets are stabilized with abundant structural proteins called oleosins, which contain a long conserved central hydrophobic hairpin structure penetrating into the matrix of the droplet. Plant lipid droplets with oleosins covering the surface are also termed oil bodies. The expression of oleosin genes is regulated individually in a tissue-specific manner in coordination with the biogenesis of oil bodies. This dissertation aims at delineating the evolution and functions of oleosins and oil bodies in primitive and advanced plant species. I have found that the primitive plant moss, Physcomitrella patens, possesses 3 oleosin genes. I have delineated the early evolution of these oleosin genes and the storage functions of the oleosin- enclosed oil bodies. In addition to seeds, tapeta of Brassicaceae also contain numerous oleosin-coated oil bodies, which are associated with flavonoid-containing vesicles to form abundant tapetosomes. Species in Brassicaceae possess a cluster of 4-7 oleosin genes specifically expressed in tapeta. I have established quantitative adaptive benefit of the oleosin gene cluster members conferring dehydration-tolerant pollen. I have found that Cleomaceae, the family closest to Brassicacea, does not possess the oleosin gene cluster or tapetosomes. Transformation of Cleomecacea with an Arabidopsis oleosin gene generates primitive tapetosomes and dehydration-tolerant pollen.

Electronic skins (E-skins) with sensory capabilities mimicking human-skin or beyond are essential for wearable healthcare monitoring devices, robotic technologies, human-machine interface (HMI) and artificial intelligence (AI). The ability to rapidly detect small pressure variation represents an important function of E-skins. Here we report a new design of pressure-sensing E-skins by integrating a unique conductive microstructured air-gap (CMAG) gate with two-dimensional (2D) semiconductor transistors to achieve unprecedented combination of ultrahigh sensitivity, rapid response, low power consumption and long-term stability. We first show that the CMAG can be used to create the capacitor-based E-skins with a highest sensitivity up to 770.4 kPa-1 (vs. ~ 4.5 kPa-1 reported previously). By employing the CMAGs as pressure sensitive gate for 2D MoS2 transistors, we show that the pressure sensitivity can be further amplified to achieve a highest value of ~ 2.61 � 107 kPa-1 (vs. the previous record of 192 kPa-1). Along with ultrafast response (< 0.1 ms), lower power consumption (~ 9 pW−270 nW), low minimum pressure detection (< 0.05 Pa) and excellent stability, our device delivers an overall performance well above the existing pressure-sensing E-skins, enabling highly sensitive static pressure mapping, real-time human pulse wave measurements, E-skin microphone for speech pattern recognition, and remote pressure monitoring.

Prostatic adenocarcinomas can recur with aggressive and lethal small cell neuroendocrine carcinoma (SCNC). Since glycolysis is a feature of malignancy and the degree generally correlates with tumor aggressiveness, glucose metabolism and the molecular mechanisms involved between the two tumor types was studied. LNCaP and PC-3 cell lines modeled adenocarcinoma and SCNC, respectively, to compare glycolytic features and metabolomics. CD44’s role was studied by RNA knockdown and overexpression. Human tissue microarrays were analyzed for glycolytic enzyme expression. Glycolytic features were found to be higher in PC-3 over LNCaP cells. PFKFB4 was overexpressed in SCNC. CD44 regulated glucose metabolism, intracellular ROS, and cell proliferation in PC-3 cells. CD44 inhibition sensitized PC-3 cells to carboplatin. The data suggests different pathways of glucose metabolism may contribute to the differing biological behaviors of the two tumor types. CD44 was found to be an important regulator of glucose metabolism in SCNC and potential therapeutic target.

Electrocatalysis plays an essential role in various clean energy technologies, including fuel cells and electrolyzers. A fundamental understanding of the electrochemical processes on the electrode and electrolyte interface is of paramount importance for rational design of advanced catalysts. Direct monitoring the interface under operando condition is the most effective way to investigate interfacial reactions, however, the interface is buried between catalyst material and electrolyte and is thus notoriously difficult to probe. Here in, I designed a faradaic transistor based on surface electron scattering effect, which provided surface specific information on the electrochemical interface. Reaction intermediates that adsorbed on catalyst surface can be directly monitored by detecting the conductance change of catalyst during a reaction. Faradaic transistor was firstly applied to investigate hydrogen adsorption process on platinum nanowires (PtNWs) surface in electrochemical environment. The hydrogen adsorption in the entire potential range, including hydrogen underpotential deposition (Hupd) and hydrogen evolution reaction (HER) region, was successfully resolved. An overpotentially deposited hydrogen (Hopd) adsorption peak near equilibrium potential was observed for the first time. DFT calculation on nanowire with same surface structure revealed the Hopd is the hydrogen that adsorbed on the edge sites, which is considered as kinetically active reaction intermediate. We later used faradaic transistor to investigate hydrogen adsorption behavior on PtNWs in solutions of different pH. A pH-dependent Hopd peak intensity was identified, which is correlated to the pH-dependent HER activity. DFT calculation also exhibited consistent results. Therefore, we proposed the HER reaction rate is governed by Hopd coverage. Further Tafel analysis demonstrated the reaction mechanism of HER is also determined by Hopd coverage. Combining theoretical and experimental results, we concluded that Hopd coverage is the unified descriptor for HER. Hydrogen adsorption on Pt/Ni(OH)2 nanowire in alkaline solution was also investigated. Compared with pure PtNWs, much higher Hopd coverage was observed on Pt/Ni(OH)2 surface in alkaline solution, which revealed the molecular-level origin of the improved HER activity on Pt/Ni(OH)2 surface. Therefore, our findings not only offered new insight into understanding the relationship between adsorbed state of reaction intermediate and reaction kinetics, but also provide a governing principle for practical catalyst design.