Industrial symbiosis: corn ethanol fermentation, hydrothermal carbonization, and anaerobic digestion

University of Minnesota M.S. thesis. December 2012. Major: Microbial Engineering. Advisor: Kenneth J. Valentas. 1 computer file (PDF); vii, 69 pages, appendices p. 50-69.The production of dry-grind corn ethanol results in the generation of intermediate products, thin and whole stillage, which require energy-intensive downstream processing for conversion into commercial co-products. Alternative treatment methods, specifically hydrothermal carbonization of thin and whole stillage coupled with anaerobic digestion were investigated to determine if they provide an opportunity to recover some of this value. By substantially eliminating evaporation of water, reductions in downstream energy consumption from 65-73% were achieved, while hydrochar, fatty acids, treated process water, and biogas co-products were generated, providing new opportunities for the industry. Processing whole stillage in this manner produced the four co-products, eliminated centrifugation and evaporation, and substantially reduced drying. With thin stillage, all co-products were again produced, as well as a high quality animal feed. Anaerobic digestion of the undiluted aqueous product stream from thin stillage hydrothermal carbonization reduced chemical oxygen demand (COD) in this product stream by more than 90% and converted 83% the initial COD to methane. Internal use of this biogas could entirely fuel the HTC process and reduce natural gas overall usage

Conformational Entropy as a Means to Control the Behavior of Poly(diketoenamine) Vitrimers In and Out of Equilibrium.

Control of equilibrium and non-equilibrium thermomechanical behavior of poly(diketoenamine) vitrimers is shown by incorporating linear polymer segments varying in molecular weight (MW) and conformational degrees of freedom into the dynamic covalent network. While increasing MW of linear segments yields a lower storage modulus at the rubbery plateau after softening above the glass transition (Tg ), both Tg and the characteristic time of stress relaxation are independently governed by the conformational entropy of the embodied linear segments. Activation energies for bond exchange in the solid state are lower for networks incorporating flexible chains; the network topology freezing temperature decreases with increasing MW of flexible linear segments but increases with increasing MW of stiff segments. Vitrimer reconfigurability is therefore influenced not only by the energetics of bond exchange for a given network density, but also the entropy of polymer chains within the network

Ion Transport and the True Transference Number in Nonaqueous Polyelectrolyte Solutions for Lithium Ion Batteries.

Nonaqueous polyelectrolyte solutions have been recently proposed as high Li+ transference number electrolytes for lithium ion batteries. However, the atomistic phenomena governing ion diffusion and migration in polyelectrolytes are poorly understood, particularly in nonaqueous solvents. Here, the structural and transport properties of a model polyelectrolyte solution, poly(allyl glycidyl ether-lithium sulfonate) in dimethyl sulfoxide, are studied using all-atom molecular dynamics simulations. We find that the static structural analysis of Li+ ion pairing is insufficient to fully explain the overall conductivity trend, necessitating a dynamic analysis of the diffusion mechanism, in which we observe a shift from largely vehicular transport to more structural diffusion as the Li+ concentration increases. Furthermore, we demonstrate that despite the significantly higher diffusion coefficient of the lithium ion, the negatively charged polyion is responsible for the majority of the solution conductivity at all concentrations, corresponding to Li+ transference numbers much lower than previously estimated experimentally. We quantify the ion-ion correlations unique to polyelectrolyte systems that are responsible for this surprising behavior. These results highlight the need to reconsider the approximations typically made for transport in polyelectrolyte solutions

From Molecules to Materials: Pre-training Large Generalizable Models for Atomic Property Prediction

Foundation models have been transformational in machine learning fields such as natural language processing and computer vision. Similar success in atomic property prediction has been limited due to the challenges of training effective models across multiple chemical domains. To address this, we introduce Joint Multi-domain Pre-training (JMP), a supervised pre-training strategy that simultaneously trains on multiple datasets from different chemical domains, treating each dataset as a unique pre-training task within a multi-task framework. Our combined training dataset consists of $\sim$120M systems from OC20, OC22, ANI-1x, and Transition-1x. We evaluate performance and generalization by fine-tuning over a diverse set of downstream tasks and datasets including: QM9, rMD17, MatBench, QMOF, SPICE, and MD22. JMP demonstrates an average improvement of 59% over training from scratch, and matches or sets state-of-the-art on 34 out of 40 tasks. Our work highlights the potential of pre-training strategies that utilize diverse data to advance property prediction across chemical domains, especially for low-data tasks

AdsorbML: A Leap in Efficiency for Adsorption Energy Calculations using Generalizable Machine Learning Potentials

Computational catalysis is playing an increasingly significant role in the design of catalysts across a wide range of applications. A common task for many computational methods is the need to accurately compute the adsorption energy for an adsorbate and a catalyst surface of interest. Traditionally, the identification of low energy adsorbate-surface configurations relies on heuristic methods and researcher intuition. As the desire to perform high-throughput screening increases, it becomes challenging to use heuristics and intuition alone. In this paper, we demonstrate machine learning potentials can be leveraged to identify low energy adsorbate-surface configurations more accurately and efficiently. Our algorithm provides a spectrum of trade-offs between accuracy and efficiency, with one balanced option finding the lowest energy configuration 87.36% of the time, while achieving a 2000x speedup in computation. To standardize benchmarking, we introduce the Open Catalyst Dense dataset containing nearly 1,000 diverse surfaces and 100,000 unique configurations.Comment: 26 pages, 7 figures. Submitted to npj Computational Material

The Flying Monkey: a Mesoscale Robot that can Run, Fly, and Grasp

The agility and ease of control make a quadrotor aircraft an attractive platform for studying swarm behavior, modeling, and control. The energetics of sustained flight for small aircraft, however, limit typical applications to only a few minutes. Adding payloads – and the mechanisms used to manipulate them – reduces this flight time even further. In this paper we present the flying monkey, a novel robot platform having three main capabilities: walking, grasping, and flight. This new robotic platform merges one of the world’s smallest quadrotor aircraft with a lightweight, single-degree-of-freedom walking mechanism and an SMA-actuated gripper to enable all three functions in a 30 g package. The main goal and key contribution of this paper is to design and prototype the flying monkey that has increased mission life and capabilities through the combination of the functionalities of legged and aerial roots.National Science Foundation (U.S.) (IIS-1138847)National Science Foundation (U.S.) (EFRI-124038)National Science Foundation (U.S.) (CCF-1138967)United States. Army Research Laboratory (W911NF-08-2-0004)Wyss Institute for Biologically Inspired Engineerin

High Quality, Low Cost Egg Incubator for BIC Church in Choma, Zambia

The Egg Incubator team is partnering with the Brethren in Christ Church located in Choma, Zambia to design a high-quality, low-cost chicken egg incubator to supply the pastors and church members with a means of food and income. The design will need to take into account the accessibility and cost of the tools and materials. The current prototype features separate heating and humidity systems, a control system to maintain a set temperature and humidity, and tilting egg racks. The heating system consists of two stovetop coils to produce heat and a fan to transfer it to the air. The humidifier utilizes an atomizer in a pan of water to create a mist that mixes with the hot air to create humidity. The control system uses a proportional integral derivative controller (PID) to keep the temperature at 37 ± 1 °C and the humidity at 60–70%. The egg racks are tilted by a motor that runs every 6 hours to prevent the embryos from sticking to the shell. With a fully functioning prototype, the team has begun to incubate 60 real fertilized eggs. During the 21-day incubation process, a final prototype iteration is being designed and will be built on-site in Zambia in May 2022. Funding for this work provided by The Collaboratory for Strategic Partnerships and Applied Research.https://mosaic.messiah.edu/engr2022/1004/thumbnail.jp

Methods of photoelectrode characterization with high spatial and temporal resolution

Materials and photoelectrode architectures that are highly efficient, extremely stable, and made from low cost materials are required for commercially viable photoelectrochemical (PEC) water-splitting technology. A key challenge is the heterogeneous nature of real-world materials, which often possess spatial variation in their crystal structure, morphology, and/or composition at the nano-, micro-, or macro-scale. Different structures and compositions can have vastly different properties and can therefore strongly influence the overall performance of the photoelectrode through complex structure–property relationships. A complete understanding of photoelectrode materials would also involve elucidation of processes such as carrier collection and electrochemical charge transfer that occur at very fast time scales. We present herein an overview of a broad suite of experimental and computational tools that can be used to define the structure–property relationships of photoelectrode materials at small dimensions and on fast time scales. A major focus is on in situ scanning-probe measurement (SPM) techniques that possess the ability to measure differences in optical, electronic, catalytic, and physical properties with nano- or micro-scale spatial resolution. In situ ultrafast spectroscopic techniques, used to probe carrier dynamics involved with processes such as carrier generation, recombination, and interfacial charge transport, are also discussed. Complementing all of these experimental techniques are computational atomistic modeling tools, which can be invaluable for interpreting experimental results, aiding in materials discovery, and interrogating PEC processes at length and time scales not currently accessible by experiment. In addition to reviewing the basic capabilities of these experimental and computational techniques, we highlight key opportunities and limitations of applying these tools for the development of PEC materials