bluuebunny/crossref_metadata_2025_split huggingface dataset:
Graph Encoders for Redox Potentials and Solubility Predictions
Authors: Furth Nicholas , Imel Adam , Zawodzinski Thomas A. 
Published: December 2024

Redox potential and solubility of organic redox-active molecules are important parameters for electrochemical devices such as aqueous and non-aqueous redox flow batteries (RFBs). The redox potential is a property of the organic molecules and is by the solvent used, while the solubility of the active species determines the volumetric capacity. When the solubility and redox potential values are multiplied, they provide insight into the energy density of the RFB, making them important properties in the feasibility and efficiency of RFBs. Other factors, such as supporting electrolytes, solvents, temperatures, and pH levels, can also affect the redox potential and solubility, making this a multi-dimensional problem. As a result, the simulation or prediction of performance characteristics for newly synthesized organic redox-active molecules is not straightforward, requiring each possible configuration of parameters, i.e., solvents,
electrolytes, and operating conditions, to be experimentally determined.
Current methods to simulate redox potential and solubility require advanced modeling techniques such as COSMO-RS, density functional theory, or molecular mechanics. Recently, machine learning (ML) models are being used to capture the intricate relationship between all parameters in complex systems such as problems that involve molecular search and property prediction. Graph neural networks (GNNs) and variational auto-encoders (VAEs) are powerful ML tools for rapidly and accurately predicting the characteristics of chemical systems. One limitation of typical GNNs is that the input can only be a single graph. We are investigating this shortcoming by utilizing multiple GNNs as part of VAE models that encode the graph inputs, which represent molecular structures, to latent space vectors. The different latent space vectors are then concatenated into a single vector, which overcomes the limitation of only permitting a single graph per input. Additionally, the different latent space vectors can be scale to account for concentrations. Our method allows the different encoder models for the redox-active material, support electrolytes, and solubility to be individually optimized while simultaneously providing a more precise optimization.
We will show how our approach allows GNNs to solve problems that consist of multiple molecular structures as inputs, such as redox potential and solubility. We then use this to predict the redox potential and solubility in various electrolyte systems to demonstrate the effectiveness of our approach. Additionally, we explore and analyze different classes of GNNs, such as graph convolutional network (GCN), crystal graph convolution neural network (CGCNN), and message passing neural network (MPNN), as the graph encoding model. Ultimately, we show that our approach provides fast and efficient predictions of complex systems and is easily transferable to other problems with multiple chemical components.
Enhanced Selectivity and Reduced Arde in Nanoscale Si Trench Etching: The Effects of Asynchronously Pulsed Plasmas, Bias Parameters, and Fluorocarbon Gas Additives
Authors: Ho Su Min , Kim Hee Ju , Kim Min Cheol , Yeom Geun Young 
Published: December 2024

The continuous miniaturization of semiconductor devices made it increasingly difficult to achieve high aspect ratio (HAR) features with precise control over the profile due to low etch selectivity over mask and aspect ratio dependent etching (ARDE). In this study, we investigated the effects of pulsed plasma modes, bias pulsing parameters, and additive gases(CF4 and C4F8) on the etching characteristics of nanoscale silicon (Si) trench using inductively coupled plasmas (ICPs) with Cl2/Ar gas mixtures.
Compared to CW plasmas, synchronously pulsed plasmas exhibit improved Si etch profiles and reduced ARDE effects. Furthermore, asynchronously pulsed plasmas showed further decreased in the ARDE effect compared to synchronously pulsed plasmas. The additive gases changed the Si trench sidewall etch profiles by protecting Si trench sidewalls during etching using asynchronously pulsed plasmas. To understand the etch mechanism of pulsed plasmas, plasmas have been characterized with high voltage probes, time resolved optical emission spectroscopy (OES), a residual gas analyzer (RGA), and a retarding field energy analyzer (RFEA). Also, the XPS measurement has been performed to understand chemical reactions on the etched material surfaces.
Experimental results demonstrated that, by controlling bias pulsing parameters and utilizing additive gases, nanoscale Si trench etch characteristics can be more precisely managed. This technique showed a promise for advanced etching applications requiring HAR features, such as nano-through silicon vias (TSVs), shallow trench isolation (STI), etc., making it a valuable method for next-generation semiconductor device fabrication.
Deep Generative Model Based Screening of Electrochemically Active Organic Compounds for Redox Flow Batteries
Authors: Ortega Ochoa Raul , Bhowmik Arghya , Vegge Tejs 
Published: December 2024

Electrochemically active organic molecules offer a promising path towards scalable solutions for sustainable energy storage, e.g., in redox flow batteries. Existing redox molecules, however, fail to combine viable redox properties with sufficient chemical and electrochemical stability. Given the multi-dimensionality and vastness of the chemical space of this set of compounds, discovering novel candidates with desired properties (sometimes referred to as inverse design) conforms a great challenge. In previous work (Materials Funnel 2.0), we demonstrate that Deep Generative Models for materials can be used together with Deep Learning surrogate models to efficiently screen for novel compounds with sought-after properties in vast chemical spaces.
In the present work we make use of such an updated funnel 2.0 approach to screen for electrochemically active organic compounds for the anode and cathode sides of a redox flow battery. We demonstrate the great utility of employing the Materials Funnel 2.0 approach to screen vast and multi-dimensional chemical spaces for novel electrochemical organic molecules with three criteria: catholyte/anolyte redox potential, water solubility, ease of synthesis. A subset of all the candidates found by our tool are then further studied to validate the approach.
Big Data for the Diagnosis and Prognosis of Deployed Energy Storage Systems
Authors: Yasir Fahim , Dubarry Matthieu 
Published: December 2024

The diagnosis and prognosis of deployed battery energy storage systems is difficult because the battery might never experience controlled conditions through normal operation as both the day-to-day charge and discharge duty cycles might be sporadic. To circumvent this issue new methodologies must be developed, and we recently proposed one for photovoltaic connected batteries. The method is using real observed solar irradiance, modeled clear sky irradiance and synthetically generated battery data from a battery digital twin to diagnose the battery degradation. The approach was demonstrated to be effective for opportunistic diagnosis without the need for any maintenance cycles for nickel manganese cobalt oxide-based batteries for days with over 50% clear sky or with an average irradiance over 650 W/m2.
This work is validating the approach further for lithium iron phosphate-based batteries. We applied our data-driven diagnosis technique to synthetically generated PV connected LFP cells and tested more than 10,000 different degradations on 720 unique days. Overall, the results showcase that, similarly to what was observed for the NMC cell, diagnosis is possible for days above 50% clear sky or for high average irradiance.
The main difference was that LAMPE estimation was proven much more problematic for LFP Cells, especially for small LAMPEs. This, however, should not hamper deployability since the analysis of the impact of the degradation composition and extent indicated that the accuracy of the diagnosis is restored when LAMPE is prominent, and thus a potential safety hazard. This additional analysis showcases the benefits of using varied synthetic datasets to investigate the impact of all possible degradation on the cells.
(Invited) Cell-Level Instrumentation of Lithium-Ion Battery Modules to Support Model Validation
Authors: Preger Yuliya , Mueller Jacob , Fresquez Armando , Rich Chaz 
Published: December 2024

Li-ion cells are the building block for most new energy storage systems. Cells are connected in series and parallel to form modules that meet the power and energy requirements of different applications. However, module design and control decisions are often based on electrochemical modeling of single cell cycling results. The predictions of these models do not always match full battery performance because the connection of cells introduces new electrical and thermal phenomena.
Public studies on module cycling are limited: most report limited cycle time, consider only a couple of cells, or lack cell-level monitoring. This talk will review experimental data on the impact of different series-parallel configurations and cell-to-cell variation on module energy throughput, current balancing, and voltage divergence over the course of hundreds of cycles. We will emphasize the impact of parallelization on module performance. This analysis was enabled by cell-level data collection of voltage, current, and temperature. The data provides a foundation to evaluate the performance trade-offs and safety implications of different module configurations, as well as validate electrochemical models of larger battery systems.

SNL is managed and operated by NTESS under DOE-NNSA contract DE-NA0003525.
Accelerating Battery Research and Development through Data-Driven Approaches: The Battery Data Alliance's Battdata Platform
Authors: Concepcion Anthony , Wang Bing Syuan , Subramanian Venkat R. , Pathak Manan , Pathak Chintan 
Published: December 2024

In the rapidly evolving landscape of battery research and development, the need for efficient, standardized, and automated testing processes has become increasingly apparent. Battery testing generates an immense volume of valuable electrochemical data, including critical parameters such as cell impedance, coulombic efficiency, capacity, and constant current/constant voltage (CC/CV) charging times. However, the lack of standardization and the limitations of traditional data management approaches have hindered the ability to extract meaningful insights from this wealth of data.
LF Energy's Battery Data Alliance (BDA) is a collaboration between industry leaders, academic institutions, and open-source communities to drive innovation and accelerate the development of advanced battery technologies. [1] BDA has developed BattData, a user-friendly battery data management framework, that enables researchers and engineers to efficiently store, process, analyze, and visualize vast amounts of battery testing data. BattData includes the module BattDB, a time series relational database designed to handle the unique requirements of battery data storage. BattDB acts as a centralized repository, allowing seamless access and querying of specific datasets. Its optimized architecture ensures high performance and scalability, making it capable of handling the ever-growing volume of battery testing data.
To streamline the data ingestion process and ensure data integrity, we introduce BattETL (Extract, Transform, Load), an open-source Python module. BattETL automates the extraction, transformation, and loading of battery cycler data into BattDB, supporting popular cycler platforms such as Maccor and Arbin. By eliminating manual data preprocessing tasks, BattETL significantly reduces the burden on researchers and minimizes the risk of errors.
In this talk, we demonstrate the benefits of the same and introduce other supporting visualization, modeling, and parameter estimation tools.

References

Battery Data Alliance – LF Energy (https://lfenergy.org/projects/battery-data-alliance/)
Crystal Morphologic Design of Na-Layered Oxide Crystals by Automated High-Throughput Flux Method Experiments
Authors: Shimizu Yusaku , Yamada Tetsuya , Yanagisawa Kazumichi , Tipplook Mongkol , Hayashi Fumitaka , Teshima Katsuya 
Published: December 2024

Na
x
CoO2 (NCO), a type of sodium layered oxide, has attracted attention as a cathode material for sodium ion batteries (SIB). The P2-type NCO has a layered structure and is generally obtained as plate-like crystals. Crystal growth of high aspect ratio NCO would be desirable to improve output density. The flux method, which is a liquid-phase method using flux (molten salt) as a solvent, is a crystal growth method that can control the crystal plane. However, in the case of NCO crystal growth, the specific effects of the flux species have not been clarified. In addition, NCO has several phases (O3, P2, P'3, etc.), depending on the Na ratio. However, fluxes easily change the Na ratio in the reaction field. For this reason, experimental guidelines for crystal control cannot be established, and trial and error must be used. The experimental search space considered by the flux method is enormous, with at least 14,000 possible experimental search spaces (three levels of raw material species and heating temperatures, nine raw material Na:Co ratios, 18 flux species and even two-component mixtures are allowed), making an exhaustive search unrealistic. To overcome this issue, it would be necessary to develop a crystal phase diagram expressing the crystal polymorphism and aspect ratio. For this purpose, identification of fluxes effective for P2 phase formation and aspect ratio changes was carried out using a data-driven, wide-area experimental search.
For the data-driven exploration, flux method experiments was conducted using the high-throughput flux method screening (HTFS) system, which was originally developed in our laboratory. In the experiments, the initial training data was collected using a grid search method, and applied to a Bayesian optimization system. In this time, regression model was constructed using the feature values extracted from X-ray diffraction (XRD) patterns as the objective variables and the experimental conditions as the explanatory variables.
About 50 experimental data were collected in a grid search using the HTFS. The formation of P2-type NCO crystals was confirmed in more than half of the samples. However, diffraction lines originating from the impurity Co3O4 were observed in some samples, confirming the strong dependence of the product on the experimental conditions. Data-driven experiments were then undertaken. A total of about 30 experiments were carried out, and in many cases P2-type crystal samples were obtained using Na-based fluxes. In addition, the aspect ratio depended on the flux species and barrel-type crystal morphology was grown in some Na-based fluxes. In this presentation, the mechanism of flux contribution to crystal growth is discussed in terms of solute-solvent interaction and surface energy.
Acknowledgement
This work was partly supported by the Strategic Innovation Program (SIP) of the Cabinet Office, and joint research with companies.
(Invited) A CMOS Inverter Technology Constructed with T-Shaped Gate Poly-Si Thin-Film Transistors
Authors: Lin Horng-Chih , Lee Cheng-Kuei , Li Pei-Wen , Chen Kung-Ming , Huang Guo-Wei 
Published: December 2024

In this work, we report a novel CMOS TFT fabrication scheme with the poly-Si TFTs featuring a T-shaped gate (T-gate), air spacers, and a self-aligned lightly doped drain (LDD). The formation of the n+ poly-Si T-gate is based on a unique process we developed recently [1]. Furthermore, by appropriately adjusting the implantation energy and dosage, we can achieve both LDD and heavily doped source/drain (S/D) regions with a single implantation process [2]. After the transistors are fabricated, the deposited oxide layer for passivation presses down on the wings of the T-gates, causing them to droop and naturally form air spacers on both sides of the gate electrodes. This can help reduce parasitic capacitance between the gate and S/D. As expected, the implementation of LDD can reduce the off-state leakage current for both n- and p-channel poly-Si TFTs as compared to the counterparts with the conventional gated configuration and without LDD. In the meantime, the n-channel T-gate poly-Si TFTs with LDD also exhibit a higher on-current than the conventional ones, thanks to the greatly shortened gated length [2]. Nonetheless, the on-current of the p-channel T-gate poly-Si TFTs with LDD is lowered despite the shorter gate length. Details about the mechanisms are explored in this work. Moreover, using n+ poly-Si gates increases the threshold voltage (Vth) of p-channel TFTs. This issue can be addressed by adjusting channel doping. Using this approach, we have successfully fabricated CMOS inverters. Figure 1 illustrates the voltage transfer characteristics (VTC) of fabricated CMOS inverters operated at 3V, confirming full-swing switching. Additionally, adjusting the channel doping of the p-channel TFT effectively tunes the switching voltage, as shown in Fig. 1.

References

[1] Y. A. Huang, C. Y. Liang, K. P. Peng, K. M. Chen, G. W. Huang, P. W. Li, and H. C. Lin, “A unique approach to generate self-aligned T-Gate transistors in counter-doped poly-Si with high etching selectivity and isotropy,” IEEE Electron Device Lett., vol. 41, no. 3, pp. 397–400,
[2] C. K. Lee, K. M. Chen, G. W. Huang, P. W. Li, and H. C. Lin, “Fabrication and characterization of T-gate polysilicon thin-film transistors with lightly-doped drain,” Jpn. J. Appl. Phys., vol. 62, SC 1009-1–5, Dec. 2022, DOI: 10.35848/1347-4065/aca773.

Figure 1
Dose-Dependent Performance of Boron-Implanted Self-Aligned Bottom-Gate IGZO TFTs
Authors: Powell Eli , Kabir Muhammad Salahuddin , Chowdhury Rahnuma Rifat , Manley Robert G. , Zhu Bin , Hirschman Karl D. 
Published: December 2024

Self-aligned channel regions in thin-film transistors (TFTs) have advantages in reduced parasitic capacitance and stage delay, and a reduction in overhead real estate. A common method used to fabricate self-aligned a-Si:H TFTs is to utilize a through-glass exposure of photoresist which is blocked by the opaque metal bottom-gate electrode. This process does not require an additional photomask or lithographic alignment, and thus supports low production cost. Sputtered IGZO has been introduced into flat panel display product manufacturing, exhibiting a channel mobility of approximately an order of magnitude higher than a-Si:H. The working source/drain electrodes in IGZO TFTs can be direct metal contact regions to the IGZO, without the need for additional processes such as doping to render the IGZO conductive. Proper metallurgy and annealing processes can provide ohmic behavior with minimal series resistance, however this usually requires several microns of gate-to-source/drain overlap to ensure such behavior.
This work provides an interpretation of donor activation in self-aligned bottom-gate IGZO TFTs with ion-implantation of boron (11B+) species as the source/drain treatment. Passivated staggered bottom gate devices were fabricated with a sputtered 50 nm IGZO channel layer. Following standard passivation processes, devices were implanted adjacent to channel regions with boron doses of 1, 2, and 4×1015 cm-2 at an energy of 35 keV. Utilizing this anneal-implant sequence resulted in self-aligned devices with transfer characteristics comparable to non-self-aligned TFTs.
Upon comparing linear mode device characteristics (Vds = 0.1 V) between the different treatments, a dose dependence was observed. Transfer characteristics appeared similar with a boron dose of 1 and 2×1015 cm-2 however, a 45% decrease in max current was observed for devices implanted with a 4×1015 cm-2 dose. From Van der Pauw measurements, a similar trend was observed; an increase in boron dose resulted in an increase in sheet resistance. A left shift in transfer characteristics was observed following thermal stress at 175 °C which is hypothesized to be the result of an interstitial boron species that remains mobile and provides a relatively low dose throughout the channel during and after thermal stress. An increased boron dose is hypothesized to result in a greater concentration of interstitial boron resulting in a greater degree of transfer characteristic shifting following thermal stress. An alternative hypothesis involves the possibility of hydrogen contamination during the implant process. The results of the investigation on the source of instability will be presented.

Figure 1
(Invited) Advancement in Neuromorphic Devices for Detection of Complex Stimuli with Low Power Consumption Utilizing Cross-Linked Electrolyte
Authors: Kim Su-Kyung , Seong Tae-Yeon 
Published: December 2024

In the quest to create computing systems that mimic the brain’s remarkable ability to process data efficiently and in parallel, researchers have recognized the need for in-memory computing devices that can overcome the limitations of the traditional von Neumann architecture. Recently, neuromorphic devices like memristors and synaptic transistors have been developed to emulate the process of neurons. However, memristors face challenges in simultaneously handling signal transmission and learning tasks. As an alternative, synaptic transistors have gained attention owing to their adjustable weight and improved non-volatile synaptic functionality. Notably, synaptic transistors with electrolyte insulating layers can form electric double layers (EDLs) with high capacitance per unit area, because of mobile ions and dipoles. This enables efficient gate-controlled synaptic behavior. Additionally, amorphous oxynitride semiconductors, used as n-type channels, exhibit superior mobility compared to other materials like 2D transition metals, perovskites, organic materials, and oxides. In our investigation, therefore, we employed a cross-linked poly-4-vinyl phenol (PVP) gate insulator to enhance the synaptic properties of oxynitride thin film transistors.
In our study, we employed zinc oxynitride (ZnON) as the channel material. ZnON offers greater stability compared to oxide materials due to its reduced oxygen vacancy concentration. We precisely controlled the solution composition and the curing process during the deposition of the electrolyte insulating layer. To optimize the formation of electric double layers (EDLs), we varied the weight ratio of poly(melamine-co-formaldehyde) methylated (PMF) and poly-4-vinyl phenol (PVP). Our investigation focused on synaptic properties, including excitatory postsynaptic current (EPSC), paired-pulse facilitation (PPF), long-term memory, and high-band filtering. All samples exhibited a counter-clockwise hysteresis curve and a memory window exceeding 2 V at 1 V of drain voltage (Vd), except for the PVP:PMF 2:1 sample. Notably, the device with a 10:1 weight ratio demonstrated the most effective EPSC response, showing a gradual increase in EPSC with repeated gate voltage (Vg) pulses. Additionally, we analyzed the EPSC characteristics under various Vg pulse conditions (width, frequency, period). To understand the behavior of internal ions and dipoles in the cross-linked PVP insulating layer, we measured the frequency-dependent capacitance. Interestingly, the capacitance decreased as the frequency increased, suggesting that internal ions could be easily influenced by electrical bias in the cross-linked PVP electrolyte, forming an EDL layer. These findings suggest that the cross-linked PVP/ZnON-based transistor could potentially serve as a significant synaptic transistor component in artificial intelligence technology.