Parametric Study on the Effect of Partial Charge on Water Infiltration Behavior in MFI Zeolites

This work analyzes the infiltration behavior of water into sub-nanometer MFI zeolite pores using molecular dynamics simulations. Infiltration simulations are run for a range of partial charge values on the zeolite atoms. Infiltration behavior is compared to partial charges to verify dependence and determine critical charge above which infiltration becomes severely inhibited even at high pressures. Attraction energy is calculated and correlated to the observed infiltration behavior. The critical partial charge of Si~1.8 occurs when the waterzeolite interaction energy becomes stronger than water-water attraction due to which water molecules get stuck and infiltration is significantly reduced. Topics: Wate

Interplay between hydrophilicity and surface barriers on water transport in zeolite membranes

A comprehensive understanding of molecular transport within nanoporous materials remains elusive in a broad variety of engineering and biomedical applications. Here, experiments and atomistic simulations are synergically used to elucidate the non-trivial interplay between nanopore hydrophilicity and surface barriers on the overall water transport through zeolite crystals. At these nanometre-length scales, these results highlight the dominating effect of surface imperfections with reduced permeability on the overall water transport. A simple diffusion resistance model is shown to be sufficient to capture the effects of both intracrystalline and surface diffusion resistances, thus properly linking simulation to experimental evidence. This work suggests that future experimental work should focus on eliminating/overcoming these surface imperfections, which promise an order of magnitude improvement in permeability.MITOR ProjectNANO-BRIDGE (PRIN 2012, grant number 2012LHPSJC)NANOSTEP (Fondazione CRT, Torino) projectsScuola Interpolitecnica di Dottorato—SCUDOISCRA initiative (CINECA award)Center for Clean Water and Clean Energy at MIT and KFUP

Probabilistic models for neural populations that naturally capture global coupling and criticality

Advances in multi-unit recordings pave the way for statistical modeling of activity patterns in large neural populations. Recent studies have shown that the summed activity of all neurons strongly shapes the population response. A separate recent finding has been that neural populations also exhibit criticality, an anomalously large dynamic range for the probabilities of different population activity patterns. Motivated by these two observations, we introduce a class of probabilistic models which takes into account the prior knowledge that the neural population could be globally coupled and close to critical. These models consist of an energy function which parametrizes interactions between small groups of neurons, and an arbitrary positive, strictly increasing, and twice differentiable function which maps the energy of a population pattern to its probability. We show that: 1) augmenting a pairwise Ising model with a nonlinearity yields an accurate description of the activity of retinal ganglion cells which outperforms previous models based on the summed activity of neurons; 2) prior knowledge that the population is critical translates to prior expectations about the shape of the nonlinearity; 3) the nonlinearity admits an interpretation in terms of a continuous latent variable globally coupling the system whose distribution we can infer from data. Our method is independent of the underlying system’s state space; hence, it can be applied to other systems such as natural scenes or amino acid sequences of proteins which are also known to exhibit criticality

Additive manufacturing of electrodes for desalination

Capacitive deionization (CDI) is an energy-efficient process for desalination of brackish (low-salinity) waters, and will be able to meet the freshwater demands of agriculture, industry, and potable water. One of the key challenges in widespread adoption of CDI is mechanical reliability of the electrodes manufactured by additive manufacturing processes. Mechanical reliability of electrodes depends on the optimal chemical composition of activated carbon-based electrode material. Traditional materials used for CDI electrodes are known to have adverse environmental effects from solvents such as N-Methyl-2-pyrrolidone (NMP) and Dimethyl sulfoxide (DMSO), and fluorine containing binders such as polyvinylidene difluoride (PVDF). In this paper we present (1) electrodes based on 'green chemistry' with reduced environmental impact, (2) stable chemical composition of electrodes with required mechanical reliability. We present the alternative CDI electrode composition using activated carbon, toluene as solvent, and polyvinyl butyral (PVB) as binder. We also mixed ion-exchange resins to produce composite electrode materials with toluene and PVB, which showed similar salt removal characteristics as composite electrodes with PVDF and NMP. Thus, the new electrode composition is a viable alternative for sustainable additive manufacturing of CDI electrodes with mechanical reliability and reduced environmental impact

Investigating the adsorption and transport of water in MFI zeolite pores for water desalination

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 71-74).The permeability of reverse osmosis membranes is limited by the diffusive transport of water across a non-porous polyamide active layer. Alternatively, fabricating a microporous active layer capable of rejecting salt ions while allowing for water transport would increase the permeability while maintaining high salt rejection. Zeolites provide a model porous network which is capable of performing this type of molecular sieve separation. However, a lack of understanding of the mechanisms that govern transport within the zeolite pore network as well as an insufficient control of membrane synthesis has limited the performance of past zeolite-based membranes. In this thesis, we seek to understand the mechanisms of water adsorption and transport in MFI-type zeolite pores through experimentation. Water adsorption on the surface and inside of the pore network was characterized by thermogravimetric analysis for varying Si/Al ratio zeolites. We estimated that the pore volume filled is -71% for a 23 Si/Al ratio MFI zeolite, -25% for an 80 Si/Al ratio MFI zeolite, and 0% for an infinite Si/Al ratio MFI zeolite. In addition, we characterized the transport of water into the MFI zeolite pore network by applying an increasing hydraulic pressure and measuring the change in volumetric displacement. We were able to corroborate the adsorbed pore volume from the TGA experiments and estimated that the pore volume filled was ~72% for a 23 Si/Al ratio MFI zeolite and ~34% for an 80 Si/Al ratio MFI zeolite. We also observed that the volumetric infiltration rate did not have an effect on the infiltration characteristics, which is distinctly different from continuum hydrodynamic behavior. Future work will focus on testing the water permeation and salt rejection of these types of zeolites. We have made significant progress in the fabrication of defect-free zeolite membranes. We also plan on investigating the adsorption and transport of water in MFI zeolite pores by using molecular dynamics simulations.by Thomas Humplik.S.M

Diffusion Augmented Agents: A Framework for Efficient Exploration and Transfer Learning

We introduce Diffusion Augmented Agents (DAAG), a novel framework that leverages large language models, vision language models, and diffusion models to improve sample efficiency and transfer learning in reinforcement learning for embodied agents. DAAG hindsight relabels the agent's past experience by using diffusion models to transform videos in a temporally and geometrically consistent way to align with target instructions with a technique we call Hindsight Experience Augmentation. A large language model orchestrates this autonomous process without requiring human supervision, making it well-suited for lifelong learning scenarios. The framework reduces the amount of reward-labeled data needed to 1) finetune a vision language model that acts as a reward detector, and 2) train RL agents on new tasks. We demonstrate the sample efficiency gains of DAAG in simulated robotics environments involving manipulation and navigation. Our results show that DAAG improves learning of reward detectors, transferring past experience, and acquiring new tasks - key abilities for developing efficient lifelong learning agents. Supplementary material and visualizations are available on our website https://sites.google.com/view/diffusion-augmented-agents/Comment: Published at 3rd Conference on Lifelong Learning Agents (CoLLAs), 202

Learning Robot Soccer from Egocentric Vision with Deep Reinforcement Learning

We apply multi-agent deep reinforcement learning (RL) to train end-to-end robot soccer policies with fully onboard computation and sensing via egocentric RGB vision. This setting reflects many challenges of real-world robotics, including active perception, agile full-body control, and long-horizon planning in a dynamic, partially-observable, multi-agent domain. We rely on large-scale, simulation-based data generation to obtain complex behaviors from egocentric vision which can be successfully transferred to physical robots using low-cost sensors. To achieve adequate visual realism, our simulation combines rigid-body physics with learned, realistic rendering via multiple Neural Radiance Fields (NeRFs). We combine teacher-based multi-agent RL and cross-experiment data reuse to enable the discovery of sophisticated soccer strategies. We analyze active-perception behaviors including object tracking and ball seeking that emerge when simply optimizing perception-agnostic soccer play. The agents display equivalent levels of performance and agility as policies with access to privileged, ground-truth state. To our knowledge, this paper constitutes a first demonstration of end-to-end training for multi-agent robot soccer, mapping raw pixel observations to joint-level actions, that can be deployed in the real world. Videos of the game-play and analyses can be seen on our website https://sites.google.com/view/vision-soccer