Applying Abundant Natural Proteins to Stabilize Lithium Anode for High-Performance Lithium-Metal Batteries

Li-metal batteries (LMBs) with ultrahigh theoretical energy densities make them promising for advanced rechargeable batteries. Despite the potential high performance, the highly reactive Li metal anode suffers from critical issues related to the dendrite growth, which shorten the cycle life and cause safety hazards of LMBs. To address these issues, modifying the surface of Li metal with functional materials is considered as an effective solution. Natural proteins are abundant polymers composed of numerous amino acids with abundant polar/nonpolar and charged/uncharged functional groups, providing diverse interactions with ions/molecules. Appropriately denatured proteins may form proper interactions with battery components (electrolyte, separator and Li plate), which enable desirable Li/electrolyte interface and guide the deposition of Li+ in LMBs for eliminating dendrite growth. This motivates us to apply protein to advance performance of LMBs in this project.This dissertation includes three parts. The first part describes the development of Li-based batteries, challenges of LMBs and proposed solutions. The second part provides the studies of protein utilization on stabilizing Li anode for high-performance LMBs, which are included in Chapters 2, 3, 4 and 5. In Chapter 2, a zein-enabled protective film with simultaneous enhancement in flexibility, modulus and adhesion strength is fabricated. The protective film is able to accommodate the volume change, reduce the side reactions, and homogenize the ion deposition of Li anode. In Chapter 3, a protein coating is created to stabilize Li anode, regulate Li+ transport, and resolve the Li dendrite growth for LMBs. The protein coating increases the wettability with electrolyte, reduces the Li/electrolyte side reactions, and significantly suppresses the Li dendrite formation. In Chapter 4, zein is exploited as an electrolyte additive to stabilize Li anode. The denatured zein is involved in the formation of solid electrolyte interphase (SEI), guiding Li+ deposition and repairing the cracked SEI. In Chapter 5, the formation and evolution of the protein-modified SEIs on Li anodes are investigated. The stand-still processes of LMBs greatly impact the formation/evolution of SEI and the resulting battery performance. In the third part (Chapter 6), the critical conclusions drawn from the studies are summarized and the outlook is proposed

Detection of extremely low concentration waterborne pathogen using a multiplexing self-referencing SERS microfluidic biosensor

Citation: Wang, C., Madiyar, F., Yu, C. X., & Li, J. (2017). Detection of extremely low concentration waterborne pathogen using a multiplexing self-referencing SERS microfluidic biosensor. Journal of Biological Engineering, 11, 11. doi:10.1186/s13036-017-0051-xBackground: It is challenging to achieve ultrasensitive and selective detection of waterborne pathogens at extremely low levels (i.e., single cell/mL) using conventional methods. Even with molecular methods such as ELISA or PCR, multi-enrichment steps are needed which are labor and cost intensive. In this study, we incorporated nano-dielectrophoretic microfluidic device with Surface enhanced Raman scattering (SERS) technique to build a novel portable biosensor for easy detection and characterization of Escherichia coli O157:H7 at high sensitivity level (single cell/mL). Results: A multiplexing dual recognition SERS scheme was developed to achieve one-step target detection without the need to separate target-bound probes from unbound ones. With three different SERS-tagged molecular probes targeting different epitopes of the same pathogen being deployed simultaneously, detection of pathogen targets was achieved at single cell level with sub-species specificity that has not been reported before in single-step pathogen detection. Conclusion: The self-referencing protocol implements with a Nano-dielectrophoretic microfluidic device potentially can become an easy-to-use, field-deployable spectroscopic sensor for onsite detection of pathogenic microorganisms

How to Retrain Recommender System? A Sequential Meta-Learning Method

Practical recommender systems need be periodically retrained to refresh the model with new interaction data. To pursue high model fidelity, it is usually desirable to retrain the model on both historical and new data, since it can account for both long-term and short-term user preference. However, a full model retraining could be very time-consuming and memory-costly, especially when the scale of historical data is large. In this work, we study the model retraining mechanism for recommender systems, a topic of high practical values but has been relatively little explored in the research community. Our first belief is that retraining the model on historical data is unnecessary, since the model has been trained on it before. Nevertheless, normal training on new data only may easily cause overfitting and forgetting issues, since the new data is of a smaller scale and contains fewer information on long-term user preference. To address this dilemma, we propose a new training method, aiming to abandon the historical data during retraining through learning to transfer the past training experience. Specifically, we design a neural network-based transfer component, which transforms the old model to a new model that is tailored for future recommendations. To learn the transfer component well, we optimize the "future performance" -- i.e., the recommendation accuracy evaluated in the next time period. Our Sequential Meta-Learning(SML) method offers a general training paradigm that is applicable to any differentiable model. We demonstrate SML on matrix factorization and conduct experiments on two real-world datasets. Empirical results show that SML not only achieves significant speed-up, but also outperforms the full model retraining in recommendation accuracy, validating the effectiveness of our proposals. We release our codes at: https://github.com/zyang1580/SML.Comment: Appear in SIGIR 202

Porous Polymer and Hybrid Materials for Efficient Liquid Phase Separation

Efficient liquid-phase separation technologies such as adsorption and membrane separation are promising to replace conventional energy-demanding separation processes. These techniques are also advantageous to deal with formidable water pollution challenges. This dissertation focuses on porous polymeric and hybrid materials that are developed as sorbents and membranes for selective adsorption, organic solvent nanofiltration, and water / oil separation applications. The first chapter introduces benchmark industrial separation technologies and current challenges in this field from the perspective of materials research. The mechanisms of selective adsorption and membrane separation are discussed. Recent advances in the applications of polymeric materials in organic solvent nanofiltration and water / oil separation are reviewed, with representative examples discussed in detail. Chapter II reports a novel and highly efficient synthetic approach to porous polymer networks, through aldol triple condensation using methanesulfonic acid as catalyst and solvent. Aromatic porous polymer networks were obtained with high porosity and narrow pore size distribution. The porous material demonstrated fast and selective adsorption of organic small molecules in aqueous solution. In addition, the pristine composition of the reaction mixture was solution processable and enabled membranes fabrication for organic solvent nanofiltration applications, as described in Chapter III. These porous polymer network membranes exhibited retained structural integrity and organic solvent nanofiltration performance with molecular-sieving selectivity and high permeability, in the presence of either a strong acid or strong base for extensive period. Chapter IV reports a hybrid membrane made of a stainless-steel mesh coated with zinc oxide tetrapod crystals and polydimethylsiloxane. The presence of micrometer-size tetrapod crystals provided a rough surface, which amplified the hydrophobicity of polydimethylsiloxane, so that the water contact angle of the membrane was greatly increased. The hydrophobic and oleophilic membrane rejected water while letting the oil permeate through, suitable for potential applications in efficient water / oil separation. Overall, this dissertation reports several examples of porous polymers and hybrid materials prepared through new synthetic and fabrication approaches. The separation mechanisms in a variety of scenarios were identified as either size-exclusion or wettability. Fundamental principles of structure-property relationships were used to guide the materials design and development. The selectivity, durability, and wettability for separation functions were tailored by engineering the porosity, aromaticity, and surface roughness of the materials, respectively. Further enhancement of the separation performances for real-life applications is anticipated through continued chemical and materials engineering approaches along this direction

Linear implicit approximations of invariant measures of semi-linear SDEs with non-globally Lipschitz coefficients

This article investigates the weak approximation towards the invariant measure of semi-linear stochastic differential equations (SDEs) under non-globally Lipschitz coefficients. For this purpose, we propose a linear-theta-projected Euler (LTPE) scheme, which also admits an invariant measure, to handle the potential influence of the linear stiffness. Under certain assumptions, both the SDE and the corresponding LTPE method are shown to converge exponentially to the underlying invariant measures, respectively. Moreover, with time-independent regularity estimates for the corresponding Kolmogorov equation, the weak error between the numerical invariant measure and the original one can be guaranteed with an order one. Numerical experiments are provided to verify our theoretical findings.Comment: 45 pages, 7 figure