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The advent of additive manufacturing (AM) processes brought with it intense research into various materials and manufacturing processes. At the same time, the need for validation of material properties, as well as study and forecasting of aging, has arisen. Modern imaging techniques, like X‐ray computed tomography (XCT), are a convenient vehicle for such studies; however, the large datasets they produce require novel analysis techniques to efficiently extract critical information. In this paper, we present our work on developing a 3D extension of the ResNet architecture to distinguish between two build orientations of tensile bars produced by AM. Using only information from XCT, our method achieves a 99.3% correct classification at a misidentification of 1%.

 

Research at the University of Washington regarding organic semiconductors is reviewed, covering four major topics: electro‐optics, organic light emitting diodes, organic field‐effect transistors, and organic solar cells. Underlying principles of materials design are demonstrated along with efforts toward unlocking the full potential of organic semiconductors. Finally, opinions on future research directions are presented, with a focus on commercial competency, environmental sustainability, and scalability of organic‐semiconductor‐based devices.

 

Forecasting models are a central part of many control systems, where high consequence decisions must be made on long latency control variables. These models are particularly relevant for emerging artificial intelligence (AI)-guided instrumentation, in which prescriptive knowledge is needed to guide autonomous decision-making. Here we describe the implementation of a long short-term memory model (LSTM) for forecasting of electron energy loss spectroscopy (EELS) data, one of the richest analytical probes of materials and chemical systems. We describe key considerations for data collection, preprocessing, training, validation, and benchmarking, showing how this approach can yield powerful predictive insight into order-disorder phase transitions. Finally, we comment on how such a model may integrate with emerging AI-guided instrumentation for powerful high-speed experimentation. 

Low C-rate charge and discharge experiments, plus complementary differential voltage or differential capacity analysis, are among the most common battery characterization methods. Here, we adapt the multi-species, multi-reaction (MSMR) half-cell thermodynamic model to low C-rate cycling of whole-cell Li-ion batteries. MSMR models for the anode and cathode are coupled through whole-cell charge balances and cell-cycling voltage constraint equations, forming the basis for model-based estimation of MSMR half-cell parameters from whole-cell experimental data. Emergent properties of the whole-cell, like slippage of the anode and cathode lithiation windows, are also computed as cells cycle and degrade. A sequential least-square optimization scheme is used for parameter estimation from low-C cycling data of Samsung 18650 NMC∣C cells. Low-error fits of the open-circuit cell voltage (e.g., under 5 mV mean absolute error for charge or discharge curves) and differential voltage curves for fresh and aged cells are achieved. We explore the features (and limitations) of using literature reference values for the MSMR half-cell thermodynamic parameters (reducing our whole-cell formulation to a 1-degree-of-freedom fit) and demonstrate the benefits of expanding the degrees of freedom by letting the MSMR parameters be tailored to the cell under test, within a constrained neighborhood of the half-cell reference values. Bootstrap analysis is performed on each dataset to show the robustness of our fitting to experimental noise and data sampling over the course of 600 cell cycles. The results show which specific MSMR insertion reactions are most responsible for capacity loss in each half-cell and the collective interactions that lead to whole-cell slippage and changes in useable capacity. Open-source software is made available to easily extend this model-based analysis to other labs and battery chemistries. 

Deep eutectic solvents (DESs) are an attractive class of materials with low toxicity, broad commercial availability, low costs and simple synthesis, which allows for tuning of their properties. We develop and demonstrate the use of high-throughput and data-driven strategies to accelerate the investigation of new DES formulations. A cheminformatics approach is used to outline a design space, which results in 3477 hydrogen bond donor (HBD) and 185 quaternary ammonium salt (QAS) molecules identified as good candidate components for DES. The synthesis methodology is then adapted to a high-throughput protocol using liquid handling robots for the rapid synthesis of DES combinations. High-throughput electrochemical characterization and melting point detection systems are used to measure key performance metrics. To demonstrate the new workflow, a total of 600 unique samples are prepared and characterized, corresponding to 50 unique DES combinations at 12 HBD/QAS molar ratios. After synthesis, a total of 230 samples are found liquid at room temperature and further characterized. Several DESs display conductivities above 1 mS cm −1 , with a maximum recorded conductivity of 13.7 mS cm −1 for the combination of acetylcholine chloride (20 mol%) and ethylene glycol. All liquid DES samples show stable potential windows greater than 3 V. We also demonstrate that these DESs are electrochemically limited by viscosity, both in the conductivity and in the limiting processes on their cyclic voltammograms. Comparison with literature reports shows good agreement for properties measured in the high-throughput study, which helps to validate the workflow. This work demonstrates new methods to accelerate the collection of key DES metrics, providing data to formulate robust property prediction models and obtaining insight on interactions between molecular components. Data-driven high-throughput experimentation strategies can accelerate DES development for a variety of applications. Moreover, these approaches can also be extended to tackle other materials challenges with large molecular design spaces.