bluuebunny/crossref_metadata_2025_split huggingface dataset:
Atomic-Scale Direct Observation on Cation Antisite Intermixing and Identification of Charge Conduction Mechanism in p-Type CuBi2O4 for Photocathodes
Authors: Lim Younghwan , Jung Hyun Joon , Park Ki Hyun , Park Chang Hyun , Chung Sung-Yoon 
Published: December 2024

Cu-based p-type semiconducting oxides have been investigated for their potential as water-reduction photocathodes in order to enhance energy conversion efficiency in photoelectrochemical cells. Among these oxides, CuBi2O4 has recently gained significant attention as a promising candidate for converting light energy into electrochemical reactions because of its good light absorption property with an adequate bandgap. Despite the importance of identifying the major defect structure to comprehend the electronic conduction behavior, however, no direct experimental analysis has been suggested yet. While the formation of Cu cation vacancies may be discussed as a possible explanation of the p-type conduction behavior, direct evidence regarding the atomic-scale structural and chemical characteristics is still required.
In this research, a combination of advanced characterization techniques is employed to figure out a representative defect structure affecting the optoelectronic properties in CuBi2O4. Cs-corrected scanning transmission electron microscopy (STEM) analysis is conducted to identify a noteworthy occurrence of antisite point defects. Atomic-level energy-dispersive X-ray spectroscopy (EDS) analysis is also employed simultaneously, to clearly show the intermixing of BiCu-CuBi antisite cations as a crucial type of point defect in CuBi2O4. Additionally, a combined result of optical and electrical measurement and density functional theory (DFT) calculations demonstrates the presence of local Cu 3d polaron states in the bandgap. A new type of charge conduction mechanism is created by the hopping of hole-polarons between Cu sites, but it is seriously hindered by the BiCu cations occupying Cu sites. A higher degree of intermixing leads to a greater activation energy and decreased electronic conductivity due to the hindrance of hole-polaron hopping, as evidenced in the Arrhenius plot.
Overall, this research provides valuable insights into the role of antisite defects in CuBi2O4 and their impact on its potential as a photocathode material. The results highlight the significance of the combination of atomic-scale structure analysis and theoretical calculations to identify the possible existence of defects and gain a comprehensive understanding of the electric characteristics in complex oxides for (photo)electrocatalysis. By understanding how antisite defects affect electronic conductivity, various strategies can be developed by controlling the possible defects to optimize not only CuBi2O4 but also other complex oxides for energy-harvesting applications.

Figure 1
Enhanced Design: A User-Friendly 3D-Printed Electrochemical Cell for In Situ Raman Spectroscopy
Authors: Milton Antony Elvin Fernando , Kawashima Kenta , Son Yoon Jun , Chang Woo Je , Wang Ziqing , Marquez Raul A. , Rose Roger F. , Milliron Delia J , Mullins Charles Buddie 
Published: December 2024

In energy conversion devices like electrolyzers and batteries, monitoring electrode surfaces during electrochemical processes is crucial for understanding their operation mechanisms. In situ Raman spectroscopy is a promising technique for this purpose, allowing analysis of electrode surface structure, surface species (reaction intermediate species), and localized electrolyte species.1 However, commercially available electrochemical cells designed for in situ Raman spectroscopy are often costly and complex, presenting a bottleneck for widespread use.
To address these issues, some research groups have introduced 3D-printed spectroelectrochemical cells with lower cost and complexity.2,3 However, existing designs typically limit the sample types to specific particles. There is a need for a more versatile, cost-effective, and user-friendly spectroelectrochemical cell that can accommodate a wider range of sample types.
This study presents an innovative spectroelectrochemical cell design capable of accommodating diverse sample types, such as metal foil strips, fluorine-doped tin oxide-coated glass, indium tin oxide-coated glass, and particles. Notably, we feature updates from the previous year's ECS fall conference (i.e., 244th ECS Meeting). Leveraging stereolithography (SLA) 3D printing with resin as the printing material, our cell design prioritizes versatility, affordability, and simplicity. Using the SLA 3D-printed spectroelectrochemical cell, we will demonstrate its practical utility and effectiveness in electrolyzer and battery applications.

References

W. Zheng, Chemistry–Methods, 3, e202200042 (2023).

M. F. dos Santos et al., Anal. Chem., 91, 10386–10389 (2019).

G. D. da Silveira et al., Anal. Chim. Acta, 1141, 57–62 (2021).
Search for Stable Structures of Adsorbed Molecules on Material Surfaces by Bayesian Optimization
Authors: Nogami Shogo , Tamura Tomoyuki , Karasuyama Masayuki 
Published: December 2024

Titanium dioxide (TiO2) is one of the most technologically promising oxides with a broad range of catalytic and photocatalytic activities, and first-principles simulations of molecular adsorption onto TiO2 surface have been extensively performed. Our group experimentally reported that the formation of an amorphous structure on the TiO2 surface enhances its catalytic activity, and is investigating the origin of high activity for amorphous surface. From the theoretical point of view, searching for stable adsorption sites on an amorphous surface model based on first-principles calculations need much high computational cost due to many adsorption sites in a large supercell. In this study, to efficiently explore stable adsorption sites on an amorphous surface, we applied Bayesian optimization, a global search method for black-box optimization, and found the most stable adsorption site of a water molecule.
First-principles calculations are performed with SIESTA code [1] with localized basis, using the generalized gradient approximation (GGA) for the exchange-correlation energy functional. The amorphous surface models of TiO2 are generated by quenching a (101) surface model of anatase-type TiO2 crystal containing 162 atoms with first-principles molecular dynamics simulation. Fig. 1 shows one of TiO2 surface models.
The adsorption energy of a molecule adsorbate is defined as
Eads=Etot
(surf+mol)-(Etot
surf+Etot
mol )
Etot
(surf+mol) is the total energy of the surface with a molecule adsorbate, Etot
surf is the total energy of the clean surface, and Etot
mol is the energy of a molecular in a vacuum.
Bayesian optimization is adopted for searching the lowest energy site with fixing TiO2 surface configurations. A Gaussian kernel and an Expected Improvement (EI) for the acquisition function are used.
Absorption energies were calculated on mesh points in the six-dimension space of the atomic positions of the oxygen (x, y, z) and rotational angles. First, we performed Bayesian optimization in the six-dimension space of the position and the angle, but this did not work well due to the discontinuity of the potential surface. Thus, we performed Bayesian optimization in the structural-descriptor space. We adopted the smooth overlap of atomic positions (SOAP) and the radial distribution function (RDF) as structural descriptors. The lowest energy sites were much efficiently found in the descriptor space, and we achieved high search efficiency.

Figure 1
(Invited) Extrinsic and Intrinsic Methods to Increase the Rate Performance of MnO2 Electrodes
Authors: Sugimoto Wataru , Takemura Yuta , Saito Ryota , Hermawan Angga , Muramatsu Keisuke 
Published: December 2024

MnO2 is one of the most well studied pseudocapacitive material for aqueous supercapacitors owing to the surface and near surface-confined redox processes. However, due to its poor electronic conductivity, a large amount of conducting additive, needs to be added to achieve high-rate charge storage. Since the conducting additive does not contribute much to the specific capacitance, this leads to decrease in total energy density of the cell in terms of both mass and volume. In addition, polymeric binders are also necessary to fabricate rigid electrode with good contact.
As an alternative to these typical conductive binders and polymer binders, we have studied the possibility of using RuO2 nanosheets as a novel inorganic binder with polymeric properties, high electronic conductivity and excellent pseudocapacitive properties. For example, by adding a small amount of RuO2 nanosheets to particulate MnO2, a large synergetic effect is observed leading to a high utilization of MnO2 pseudocapacitance.1 The optimized amount of RuO2 in the composite was 13 wt%, which is much lower than the amount generally used for conductive carbon additives. The size of nanosheet binders also affects the properties of the electrode. Using MnO2 nanosheets and RuO2 nanosheets with different lateral sizes, it has been shown that larger sized nanosheets affords better conductive binder properties through a better electronic conduction network structure.
Asides from using conductive binders, heterogeneous doping can increase the intrinsic conductivity of MnO2. We have shown that Ir-doped Birnessite shows faster redox behavior than pristine MnO2 and this enhanced kinetics are reflected on the exfoliated nanosheets. These properties can be discussed based on the electronic interaction between Mn and Ir.
The pseudocapacitive performance of MnO2 can also be manipulated by inducing cationic defects in Birnessite. Low temperature annealed MnO2 with higher number of defects shows higher specific capacitance at low rate, albeit at the loss of high-rate performance. This trade-off is discussed based grain-boundary resistance dominating the rate performance.

Acknowledgments

This work was supported in part by ISHIFUKU Metal Industry Co., Ltd. and a Japan Science and Technology Agency Program on Open Innovation Platform with Enterprises,
Research Institute and Academia (JST OPERA JPMJOP1843).

References:

Saito, Y. Sato, D. Takimoto, S. Hideshima, W. Sugimoto, Electrochemistry, 88(3), 107 (2020).

Saito, H. Tanaka, K. Teshima, D. Takimoto, S. Hideshima, W. Sugimoto, Inorg
.
Chem., 61, 4566 (2022).
Surface and Defect Modulation via Flame Technique for Efficient Photoelectrochemical Water-Splitting
Authors: Tan Runfa , Jeong YooJae , Cho In Sun 
Published: December 2024

The conversion of solar energy into chemical fuel via photoelectrochemical (PEC) water splitting shows great promise for solving the energy crisis, achieving carbon neutrality, and a sustainable future. Iron (Fe)-based materials have attracted tremendous attention from various light absorber materials due to their earth abundance, high light absorption, and robust PEC stability under harsh conditions[1]. However, diverse shortcomings, such as the short hole diffusion length, severe bulk/interface recombination, and sluggish water oxidation kinetics, limit the photocurrent generation for achieving high solar-to-hydrogen efficiency[2,3]. In particular, the high-temperature annealing requirement necessitates suitable synthesis and activation strategies to awaken their PEC activity.
In this talk, we present a novel high-temperature flame technique to activate two Fe-based model photoanodes, i.e., zinc ferrite (ZnFe2O4, ZFO) and hematite (Fe2O3). The flame-activated ZFO (just a few tens of seconds) exhibits an enhanced charge collection ability and enriched active sites compared to the non-activated ZFO. These improvements are attributed to the high-temperature induced grain growth, enhanced grain connection, and rich defect formation caused by the fuel-rich flame environment[4]. On the other hand, a unique textured and mesoporous hematite (tm-hematite) photoanode can be synthesized via the same flame technique combining solution chemistry. The resultant tm-hematite exhibited enlarged surface area, enhanced charge transport, and transfer properties. We show that the unique mesoporous morphology, dominated (110) plane, and rich Ti dopant concentration on grain surface are responsible for the enhanced PEC activity. Notably, the tm-hematite showed greatly enhanced photocurrent density (3.1 mA/cm2 at 1.23 V vs the reversible hydrogen electrode, RHE) compared to the conventional nanorod hematite (~1.4 mA/cm2 at 1.23 VRHE) under 1 sun illumination. The flame-activated ferrite photoanodes demonstrated excellent PEC stability for 50 hours, even without additional coating/treatments. Our flame technique shows extraordinary potential for synthesizing high-efficiency ferrite-based photoelectrodes and puts a step forward to achieving the goal of sustainable energy and environment future.

Keywords: Photoelectrochemical water splitting, Ferrites, Flame treatment, defect control, mesoporous structure, charge collection, photocurrent density, stability

References

[1] R. Tan, A. Sivanantham, B. J. Rani, Y. J. Jeong, I. S. Cho, Coord. Chem. Rev.
2023, 494, 215362.
[2] I. S. Cho, M. Logar, C. H. Lee, L. Cai, F. B. Prinz, X. Zheng, Nano Lett.
2014, 14, 24.
[3] I. S. Cho, H. S. Han, M. Logar, J. Park, X. Zheng, Adv. Energy Mater.
2016, 6, 1501840.
[4] R. Tan, Y. J. Jeong, Q. Li, M. Kang, I. S. Cho, J. Adv. Ceram.
2023, 12(3): 612-624.

Figure 1
(Invited) Innovation in Heterogeneous Catalysis: Constructive Interference at the Interface
Authors: Leddy Johna 
Published: December 2024

Reaction rates are increased by better catalysts and by introduction of energy as heat or pressure. In electrochemical systems, energy is introduced as electrical potential that drives electron transfer across the electrode solution interface. In sonochemical systems, energy is imparted to bulk solvents by ultrasound that generates cavitation bubbles; as cavitation bubbles collapses extremes of temperature (> 5000 C) and pressure (> 1000 atm) are induced in a narrow domain about the collapsed bubble.
By coupling sonochemistry and electrochemistry, extremes of pressure are deposited at the electrode solution interface to increase electron transfer rates and to remove passivating layers such as oxides from the electrode surface.
The system is built as a thin layer electrochemical cell where sonicator and electrode are separated by about the wavelength of the ultrasound, on the order of a 1 cm. When properly aligned, constructive interference builds pressure at the electrode solution interface at a rate of about 1 J (mole s)-1. The oscillators operate in the range of 40 kHz, with little parasitic loss.
Constructive interference in thin layer sonoelectrochemical cells substantially increases rates of O2 reduction and alcohol electrolysis.
The financial support of the National Science Foundation (NSF CHE-1309366) is acknowledged.

References

Chester G. Duda, Brenna Parke, and Johna Leddy, “Thin Layer Sonoelectrochemistry:Impact on Slow Heterogeneous Electron Transfer,” Electrochimica Acta (2023) 468 143118 https://doi.org/10.1016/j.electacta.2023.143118.

Daniel L. Parr IV, Chester G. Duda, and Johna Leddy, “Why Sonochemistry in a Thin Layer? Constructive Interference," J. Physical Chemistry C (2023) 127(25) 12184-12193, https://doi.org/10.1021/acs.jpcc.3c00804.

Daniel L. Parr IV and Johna Leddy, “To Configure Thin Layer Sonoelectrochemical Experiments,” Journal of the Electrochemical Society (2021) 168(6) 066524, https://iopscience.iop.org/article/10.1149/1945-7111/ac0861.

J. Leddy, C. G. Duda, J. Lyon, and W. J. Leddy III, “Thin layer sonochemistry and sonoelectrochemistry devices and methods,” US Patent 10,300,453, May 2019, Assignee: Johna Leddy, https://patents.google.com/patent/US10300453.
(Invited) Active Learning Strategies for Teaching Electrochemical Energy Conversion and Storage: Insights from Science Clubs
Authors: Marquez Raul A. , Kalokowski Emma , Espinosa Michael , Ramos-Sanchez Victor Hugo , Rodriguez Luis Carlos , Valenzuela de la Rosa Fernando , Mullins C Buddie 
Published: December 2024

Electrochemistry is rapidly growing, yet its crucial role in emerging industries and research is not sufficiently mirrored in the curriculum of US and Mexican universities. While undergraduate students are briefly exposed to basic electrochemistry concepts within general and physical chemistry courses, dedicated electrochemistry education is often reserved for graduate programs. Furthermore, teaching electrochemistry is challenging as it is a multifaceted subject encompassing concepts from various disciplines. Despite this complexity, electrochemistry equips students with invaluable skills in analytical chemistry, experimental design, instrumental analysis, and device engineering. Recognizing the need for a skilled workforce to meet the demands of emerging industries, it is imperative to reinvigorate electrochemistry education and introduce it at the early undergraduate level to foster interest and deeper engagement.
To bridge this educational gap, we have developed a science workshop focusing on teaching electrochemical energy conversion and storage concepts to high school and early undergraduate students. As part of Clubes de Ciencia MX, a Mexican initiative promoting science and engineering summer workshops, this unique experience has utilized active learning strategies to teach and encourage electrochemistry for two consecutive years. The workshop covers five fundamental areas (Figure 1): electrochemistry basics, water electrolysis, electrocatalysis, fuel cells, and battery technology. By integrating short lectures, group discussions, and interactive learning through experiments, games, and project-based activities, we strive to ignite students' passion for electrochemistry. We have also conducted assessments to examine the workshop's effectiveness and influence on students' understanding of the role of electrochemistry in climate empowerment and sustainability. This unique experience aims to captivate and inspire early-career students, nurturing the next generation of scientists and engineers who will revolutionize our world.

Figure 1
(Invited) Automation of Electrochemical Water Desalination Processes Using Artificial Intelligence Technologies
Authors: Son Moon 
Published: December 2024

Electrochemical desalination technologies, including battery electrode deionization and capacitive deionization (CDI), are being actively studied as alternatives to membrane technology due to their relatively low energy consumption. In line with the development of technology, modeling-based research has also been actively conducted to mathematically simulate and automatically control the technology (Donnan models or Lanmuir models). However, despite the complexity of calculations, these numerical models cannot simulate the entire actual operating environment; therefore, multiple assumptions are required. In addition, even in this simplified environment, predictions for specific parameters (effluent pH, etc.) are often very inaccurate.
Therefore, research on big data-based artificial intelligence technology that can overcome the shortcomings of these numerical models has been reported mainly by specific research groups in recent years. First, a study that successfully predicted effluent concentration and pH in CDI using deep learning technology was reported in 2021 yr (i.e., Convolutional Neural Network combined with Long Short-Term Memory), followed by a study based on an ensemble model (i.e., Random Forest) that can overcome the complexity of deep learning models in 2023 yr. In addition, reinforcement learning-based research that can automate processes is also being reported starting in 2022 yr.
Therefore, in this presentation, artificial intelligence technologies that can predict specific output variables of electrochemical desalination technology, quantify the correlation between each input variable (xAI), and ultimately enable process automation will be discussed. The advantages and disadvantages of different artificial intelligence technologies will also be discussed from a practical/technical perspective (i.e., response time, computational cost, etc).
(Invited) Comparison of Tree-Based Model with Deep Learning Model for Predicting Effluent pH and Concentration in an Electrochemical Water Desalination Process
Authors: Ullah Zahid , Yun Nakyeong , Son Moon 
Published: December 2024

Capacitive deionization (CDI) is an emerging water treatment technology that offers potential advantages, including reduced environmental impact, simpler operation, and lower energy consumption in certain applications. However, for optimizing the process and ensuring high-quality treated water, accurate prediction of effluent pH and concentration in CDI systems is crucial. Various numerical models developed for simulating CDI performance often struggle to capture the complexities of real-world operating conditions. Recently, artificial intelligence (AI) techniques have been widely employed to overcome these limitations by their capability to model complex nonlinear relationships and adaptively respond to changing conditions. Therefore, this study presents a comprehensive comparison of tree-based models and a deep learning model in predicting the effluent pH and concentration in a CDI system. The study developed and evaluated the performance of a steady random forest (SRF) model, a dynamic random forest (DRF) model, and a convolutional neural network-long short-term memory (CNN-LSTM) model using the same dataset. The models were trained and tested on tabular data from constant current (CC) and constant voltage (CV) MCDI operations.
The results showed that the tree-based models and the CNN-LSTM model achieved similar prediction accuracy when used on the operation mode they were trained on, with R2 values exceeding 0.98 for both effluent pH and concentration. However, in generalization tests across different operation modes, the performance of the SRF model without binary features significantly deteriorated (R2 < 0.115). To address this, binary features were incorporated to distinguish between the CC and CV operating modes, which substantially improved the SRF model's generalization ability, with R2 values (model R2 > 0.5) comparable to the CNN-LSTM model.
Feature importance analysis revealed that voltage, current, and influent pH were the most influential factors in predicting both effluent pH and concentration, aligning with the characteristics of the CDI process. Notably, the tree-based models demonstrated a significant advantage in computational efficiency, requiring over 100 times less computation time per iteration than the CNN-LSTM model.
These findings suggest that tree-based models, particularly the SRF model with binary features, can be a viable and computationally efficient alternative to deep learning models for modeling and optimizing CDI processes. The approach of incorporating binary features to enhance the generalization ability of tree-based models is a practical and cost-effective solution that can be applied to other complex engineering problems. Further research in this area could help reduce modeling complexity and computational costs, offering more straightforward computational modeling methods for CDI processes.