Study of A Humidity-Swing Carbon Dioxide Sorbent

Hydration of neutral and ionic species at interfaces plays an important role in a wide range of natural and artificial, fundamental processes, including in energy systems as well as biological and environmental systems. Owing to the hydration water at the interface, the rate and extent of various types of chemical reactions may be significantly enhanced. The hydration of ions does not only affect the physical structure and dynamics of water molecules, but also chemical energy transfers through the formation of highly structured water complexes that form in the bulk water. Indeed, dehydration could promote the energy levels of aqueous compounds. These shifts in energy states may receive wide applications such as in energy storage with anhydrous salts, enhancement of the free energy of binding ligands to biological systems, and gas separation using a water-modified basicity of ionic sorbents. Of particular interest in this study is a novel technology for direct air capture of carbon dioxide, driven by the free energy difference between the hydrated and dehydrated states of an anionic exchange resin and its effect on the affinity of CO2 to the resin. In this dissertation, we first demonstrate an unconventional reverse chemical reaction in nano-confinement, where changes in the amount of hydration water drive the direction of an absorption/desorption reaction, and apply this novel mechanism of controlling the behavior of a sorbent to air capture of CO2. The reduction of the number of water molecules present in the pore space promotes the hydrolysis of CO32- to HCO3- and OH-. This phenomenon has led to a nano-structured CO2 sorbent that binds CO2 spontaneously in ambient air when the surrounding is dry, while releasing it when exposed to moisture. We name this phenomenon of loading and unloading a sorbent with water a hydration swing. Wide application of hydration swings to absorb CO2 requires a detailed understanding of the molecular mechanisms of the hydration induced energy change at the ion hydration/solid interface. Using atomistic simulations, the mechanism of CO2 absorption with respect to water quantity was elucidated via the explorations of the reaction free energy of carbonate ion hydrolysis in a confined nano-environment. Next, based on the understanding of the underlying driving mechanism, a systematic study of the efficiency of effective hydration-driven CO2 capture with respect to different pore sizes, hydrophobic/hydrophilic confined layers, temperatures, and distances of cations may further benefit the optimization of the CO2 capture system, in terms of the energetically favorable states of hydration ions in dry and wet conditions. This part of the research may sheds some insights on future research of designing high efficiency CO2 capture sorbent according to adjust the above described parameters. This unconventional reverse chemical reaction is not restricted to carbonate ions in nano-confined space. This is an universal phenomenon where hydrated ions carrying several water molecules in nanoscopic pores and in the natural atmosphere under low relative humidity. Such formations of hydrated ions on interfaces with the high ratio of ions to water molecules (up to 1:1) are essential in determining the energetics of many physical and chemical systems. In this dissertation, we present a quantitative analysis of the energetics of ion hydration in nanopores based on computational molecular modeling of a series of basic salts with the different quantities of water molecules. The results show that the degree of hydrolysis of basic salts with several water molecules is significantly different from the conventional degree of hydrolysis of basic salts in bulk water. The reduction of water molecules induces divalent and trivalent basic ions (S2-, CO32-, SO32-, HPO42-, SO42-, PO43-) to hydrolyze water into a larger amount of OH- ions, conversely, it inhibits monovalent basic ions (CN-, HS-) from hydrolyzing water. This finding opens a vast scope of new chemistry in nanoconfined water. Ion hydrations containing interfaces play an important role in a wide range of natural and fundamental processes, but are much less noticeable currently. This thesis sheds some lights on a vast number of chemical processes of hydrated ion pairs containing interfaces, and design possibility for more efficient energy-saving sorbents

Development of a Transferable Reactive Force Field of P/H Systems: Application to the Chemical and Mechanical Properties of Phosphorene

ReaxFF provides a method to model reactive chemical systems in large-scale molecular dynamics simulations. Here, we developed ReaxFF parameters for phosphorus and hydrogen to give a good description of the chemical and mechanical properties of pristine and defected black phosphorene. ReaxFF for P/H is transferable to a wide range of phosphorus and hydrogen containing systems including bulk black phosphorus, blue phosphorene, edge-hydrogenated phosphorene, phosphorus clusters and phosphorus hydride molecules. The potential parameters were obtained by conducting unbiased global optimization with respect to a set of reference data generated by extensive ab initio calculations. We extend ReaxFF by adding a 60{\deg} correction term which significantly improves the description of phosphorus clusters. Emphasis has been put on obtaining a good description of mechanical response of black phosphorene with different types of defects. Compared to nonreactive SW potential [1], ReaxFF for P/H systems provides a huge improvement in describing the mechanical properties the pristine and defected black phosphorene and the thermal stability of phosphorene nanotubes. A counterintuitive phenomenon is observed that single vacancies weaken the black phosphorene more than double vacancies with higher formation energy. Our results also show that mechanical response of black phosphorene is more sensitive to defects for the zigzag direction than for the armchair direction. Since ReaxFF allows straightforward extensions to the heterogeneous systems, such as oxides, nitrides, ReaxFF parameters for P/H systems build a solid foundation for the reactive force field description of heterogeneous P systems, including P-containing 2D van der Waals heterostructures, oxides, etc

Skill-based Model-based Reinforcement Learning

Model-based reinforcement learning (RL) is a sample-efficient way of learning complex behaviors by leveraging a learned single-step dynamics model to plan actions in imagination. However, planning every action for long-horizon tasks is not practical, akin to a human planning out every muscle movement. Instead, humans efficiently plan with high-level skills to solve complex tasks. From this intuition, we propose a Skill-based Model-based RL framework (SkiMo) that enables planning in the skill space using a skill dynamics model, which directly predicts the skill outcomes, rather than predicting all small details in the intermediate states, step by step. For accurate and efficient long-term planning, we jointly learn the skill dynamics model and a skill repertoire from prior experience. We then harness the learned skill dynamics model to accurately simulate and plan over long horizons in the skill space, which enables efficient downstream learning of long-horizon, sparse reward tasks. Experimental results in navigation and manipulation domains show that SkiMo extends the temporal horizon of model-based approaches and improves the sample efficiency for both model-based RL and skill-based RL. Code and videos are available at \url{https://clvrai.com/skimo}Comment: Website: \url{https://clvrai.com/skimo

Multimodal-Enhanced Objectness Learner for Corner Case Detection in Autonomous Driving

Previous works on object detection have achieved high accuracy in closed-set scenarios, but their performance in open-world scenarios is not satisfactory. One of the challenging open-world problems is corner case detection in autonomous driving. Existing detectors struggle with these cases, relying heavily on visual appearance and exhibiting poor generalization ability. In this paper, we propose a solution by reducing the discrepancy between known and unknown classes and introduce a multimodal-enhanced objectness notion learner. Leveraging both vision-centric and image-text modalities, our semi-supervised learning framework imparts objectness knowledge to the student model, enabling class-aware detection. Our approach, Multimodal-Enhanced Objectness Learner (MENOL) for Corner Case Detection, significantly improves recall for novel classes with lower training costs. By achieving a 76.6% mAR-corner and 79.8% mAR-agnostic on the CODA-val dataset with just 5100 labeled training images, MENOL outperforms the baseline ORE by 71.3% and 60.6%, respectively. The code will be available at https://github.com/tryhiseyyysum/MENOL.Comment: 7 pages,6 figure