bluuebunny/crossref_metadata_2025_split huggingface dataset:
Thermal Characterization of Large-Format Li-Ion Pouch Cells with Transient Cooling and Lock-in Thermography
Authors: Lin Jie , Chu Howie , Monroe Charles W. , Howey David 
Published: July 2021

Electrification of transport and heating is an important part to combat climate change. This leads to an increasing demand for energy storage options for power grids and electric vehicles.
Recent progress in battery management systems, and energy density improvements, have favored the deployment of Li-ion batteries as the leading electrochemical energy storage technology [1]. At the same time, battery thermal management is a critical challenge due to inevitable heat generation from ohmic, reaction and entropic heating [2]. If the amount of heat generated is not sufficiently dissipated from the battery, it could substantially increase the cell temperature which leads to severe degradation and even catastrophic thermal runaway [3, 4]. Especially for large-format pouch cells, high temperature and large temperature non-uniformity are common issues and thus require careful battery thermal design. One tool to address this is by establishing an electrochemical-thermal model of performance – but this requires accurate knowledge of various battery properties. Commercial Li-ion batteries have a multi-layered unit-cell structure in jelly roll or stack, with each unit cell containing current collectors, cathode, anode and separator. The cell components with different properties, multiphase electrodes and complex electrode morphology have made it difficult to obtain battery properties without proper characterization methods.
Therefore, in this study, we propose novel thermal characterization approaches to measuring the specific heat capacity and anisotropic thermal conductivity of large-format Li-ion pouch cells. The specific heat is characterized via a transient cooling method together with an analytical model, while the thermal diffusivity can be obtained by fitting a 3D thermal model to the dynamic surface temperature response measured with lock-in thermography. A nonlinear least-squares optimization algorithm is used to parameterize the battery thermal diffusivity. After that, the battery thermal conductivity can be decoupled from the measured specific heat and thermal diffusivity. Validated with aluminum reference samples, the present characterization methods have the merits of fast speed and good accuracy, which will be appealing to both scientific research and engineering practice.

References

G. Zubi, R. Dufo-López, M. Carvalho and G. Pasaoglu, Renewable and Sustainable Energy Reviews, 89, 292 (2018).

D. Bernardi, E. Pawlikowski and J. Newman, J Electrochem Soc, 132, 5 (1985).

D. P. Finegan, E. Darcy, M. Keyser, B. Tjaden, T. M. M. Heenan, R. Jervis, J. J. Bailey, R. Malik, N. T. Vo, O. V. Magdysyuk, R. Atwood, M. Drakopoulos, M. DiMichiel, A. Rack, G. Hinds, D. J. L. Brett and P. R. Shearing, Energy & Environmental Science, 10, 1377 (2017).

Y. Zhu, J. Xie, A. Pei, B. Liu, Y. Wu, D. Lin, J. Li, H. Wang, H. Chen, J. Xu, A. Yang, C. L. Wu, H. Wang, W. Chen and Y. Cui, Nat Commun, 10, 2067 (2019).
Modelling the Growth and Etch of Thin Film Metal Oxides
Authors: Mullins Rita , Kondati Natarajan Suresh , Nolan Michael 
Published: July 2021

HfO2 and ZrO2 are two high-k materials that are crucial in semiconductor devices. Atomic level control of material processing is required for fabrication of thin films of these materials at nanoscale device sizes. Atomic layer deposition (ALD) and thermal atomic layer etching (ALE) allow fabrication of ultra-thin films for semiconductor device processing. ALD is a well-known metal oxide thin film deposition technique in which metal and oxygen containing precursors are added sequentially to the reactor to ensure self-limiting precursor adsorption and reaction to enable a high level of control over film thickness. Thermal ALE, which is a relatively modern technique, uses self-limiting fluorination (using hydrogen fluoride exposure) and subsequent ligand exchange reactions at elevated temperatures to remove up to a monolayer of the metal oxide material. This modern approach for controlled etching is the reverse of ALD and removes only the fluorinated layer.
Given that it is difficult to investigate ALD and ALE reactions directly using experimental techniques, first-principles-based atomic-level simulations using density functional theory (DFT) can give deep insights into the precursor chemistry and the reactions that drive the deposition and etch of different materials. This contribution presents first principles density functional theory modelling to examine the growth and etch of thin films of HfO2 and ZrO2. Concerning the ALD reaction, the adsorption mechanism of precursors TDMAHf and TDMAZr are studied at the bare and hydroxylated surfaces of HfO2 and ZrO2 respectively. Stable OH coverages of 0.50 ML and 0.63 ML on the surfaces of HfO2 and ZrO2 were found respectively. Ligand loss from the metal precursor can be achieved by protonation from the OH terminated surface of both metal oxides. Concerning the ALE reaction, HF exposures on the surfaces of HfO2 and ZrO2 are studied. HF coverages ranging from 1.0 ± 0.3 to 17.0 ± 0.3 HF/nm2 are investigated and a mixture of molecularly and dissociatively adsorbed HF molecules is present at higher coverages. Theoretical etch rates of -0.61 ± 0.02 Å /cycle for HfO2 and -0.57 ± 0.02 Å /cycle ZrO2 were calculated using maximum coverages of 7.0 ± 0.3 and 6.5 ± 0.3 M-F bonds/nm2 respectively (M = Hf, Zr).
(Invited) Area-selective spatial ALD of SiO2 interleaved with back-etch corrections: Selectivity and surface inspection of non-growth area
Authors: Mameli Alfredo , Karasulu Bora , van Es Maarten , Roozeboom Fred 
Published: July 2021

The combination of area-selective ALD intermitted with back-etching steps has recently attracted much interest because of its potential in achieving high selectivity. Compared to non-interrupted area-selective ALD this supercycle approach has proven to be very effective in reducing the defectivity on the non-growth areas and thickening the layers on the growth areas.1, 2
In this work we studied the case of selective SiO2 deposition on silicon substrate wafers (growth area) with large ZnO patterns (non-growth area). We will demonstrate the effectiveness of this complementary approach, i.e., deposition and etch-back corrections in a rotary atmospheric-pressure all-spatial processing (= ‘dep/etch’) reactor (Fig. 1) to maximize SiO2 selectivity while obtaining high deposition rates. For the selective spatial ALD part of the supercycle a three-step (‘ABC’-type) recipe was adopted,3 consisting of exposure to: A) inhibitor, B) silicon precursor (BDEAS) and C) O2 plasma. The periodic back-etch intermissions in the supercycle were carried out in the same integrated spatial process reactor by exposing the substrate to a CF4-O2-N2 plasma. The plasma source used for both O2-plasma and the back-etch was of the atmospheric-pressure Dielectric Barrier Discharge (DBD) type4.
Figure 2 gives an illustration for a supercycle process with the substrate rotating at 20 rpm and using 3 repetitions of 40 ‘ABC’-cycles and 6 corrective back-etch cycles. During the each set of 40 ‘ABC’-cycles we could selectively grow sufficient (i.e., up to ~3.5 nm) SiO2 layer thickness on the growth area before the first nucleation of SiO2 became apparent on the ZnO non-growth areas. This way some selectivity is gradually lost with the number of ALD cycles increasing. As shown in Fig. 2 the selectivity loss can be repeatedly restored by a CF4-O2-N2 plasma back-etch with no silicon detected on the non-growth area by XPS. We demonstrated that with 3 repeated ‘dep/etch’ supercycles up to 10 nm thick SiO2 could be grown on the growth area.
We will discuss how Low Energy Ion Spectroscopy (LEIS), with its extreme sensitivity to the top monolayer(s) of a thin film, can give more and reliable quantitative information on the process selectivity and the defectivity on the non-growth area in terms of surface coverage and derived thickness. In addition, scanning probe FTIR-AFM microscopy can give information about the defects and contaminants at the substrate surface. Furthermore, we extended the selective spatial ALD of SiO2 to other oxidic non-growth areas like Al2O3, IGZO, SnO2, Ta2O5 and ZrO2.
1. R. Vallat, et al., J. Vac. Sci. Technol. A, 35, 01B104 (2017).
2. S. K. Song, et al., Chem. Mater. 31, 4793-4804 (2019).
3. A. Mameli, et al., ACS Nano, 11, 9303-9311 (2017).
4. Y. Creyghton, P. Poodt, M. Simor, F. Roozeboom, US Patent Application 20170137939.

Figure 1
Microplasma-Engineered Nanoassembly of Core-Shell Plasmonic Nanoparticles for Ultrasensitive Flexible Surface-Enhanced Raman Scattering Substrates
Authors: Yeh Yi-Jui , Chiang Wei-Hung , Tung Kuo-Lun 
Published: July 2021

Surface enhance Raman scattering (SERS) is emerging as a powerful technology for the label-free detection of various biological and chemical species. One of the important but often overlooked considerations in the design of surface-enhanced Raman scattering (SERS) substrates for trace detection is the efficiency of sample collection. Conventional designs based on rigid substrates such as silicon, alumina, and glass resist conformal contact with the surface under investigation, making the sample collection inefficient.
The aim of our work is to develop an effective paper-based test strip to achieve high SERS performance for biosensing. Here we present one-step green-fabrication of bimetallic nanoparticles Au/Ag (3D porous stricture) loaded on filter paper using microplasma system. This approach requires a minimal fabrication effort and cost, also contains nanoisland and nanogaps (<20nm) on paper substrate to generate a high density of hot spot(~2000/um2). The 3D biomimetric substrate shows a low detection limit for Rhodamine 6G, high averaged enhancement factor, excellent signal uniformity, good stability and suitability in biosensing. In addition, the novel substrate was applied in label-free folic acid, dopamine and other Raman probe detection with small amount and low concentration of analyte, and it exhibited effective SERS platform potential for bio-detection and bio-sensing.

Figure 1
Digging in Documents: Using Text Mining to Reveal Hidden Knowledge in the Lithium-Ion Battery Literature
Authors: EL-Bousiydy Hassna , Lombardo Teo , N. Primo Emiliano , Duquesnoy Marc , Morcrette Mathieu , Johansson Patrik , Simon Patrice , Grimaud Alexis , A. Franco Alejandro 
Published: July 2021

Scientific publications constitute the main information source of battery-related data for academic research. There is an impressive number of papers published on Lithium Ion Batteries (LIBs) for instance. Such impressive amount of data in principle could be used by machine learning (ML) methods in order to optimize efforts and even make possible the discovery of new optimal experimental conditions. Yet, the majority is published as unstructured data, making ML analysis difficult. Here, in order to unravel researchers’ habits when reporting their results, we present a text mining study using primarily publications containing original experimental data from the LIB field. We analyze the reported data structure and we discuss the remaining challenges for applying automated knowledge discovery techniques. We then present a Named Entity Recognition approach to automatically extract quantified parameters from the battery literature and how to build an in house generated database containing a wide diversity of materials, experimental parameters and conditions. Our in house approach achieves a data extraction F-scores ≥ 90% and can thereby pave the way towards an automatized way of generating large databases for use in battery ML models.

References
El-Bouysidy, H., Lombardo, T., Primo, E. N., Duquesnoy, M., Morcrette, M., Johansson, P., Simon, P.,
Grimaud, A., & Franco, A. A. submitted (2020).
Torayev, Amangeldi, Pieter CMM Magusin, Clare P. Grey, Céline Merlet, and Alejandro A. Franco. Journal of Physics: Materials 2, no. 4 (2019): 044004.
Microplasma Assembly a High-Efficient and Catalytic Platform Reduction 4-Nitrophenol Based on Reusable Au@Ag Nanoparticle / Cellulose Paper
Authors: Yeh Yi-Jui , Chiang Wei-Hung , Tung Kuo-Lun 
Published: July 2021

Grafting metal nanoparticles (NPs) on flexible platforms is being increasingly attempted to advance their usage in the field of catalysis, sensing, energy storage, etc. However, anchoring NPs on substrates is nontrivial as it involves surface modification of the NPs and/or the substrate, which makes the whole process tedious. On the other hand , Bimetallic nanoparticles, which are composed of two metal elements in a particle, exhibit much higher catalytic activity than respective monometallic ones due to new bi-functional or synergistic effects, so-called a ligand and/or an ensemble effect.
Here we present the green, facile, less-time, one-step process fabrication of 3-dimension porous Au(core)@Ag(shell) NP /filter paper by Microplasma-assisted system. The aim of our work is to develop a high efficient catalytic substrate on common filter paper loaded on Au@Ag NPs. In our work, we found Au@Ag NP /filter paper can be synthesized and in a minute time scale. We further extend this technology to fabricated Au@Ag NP /paper and characterized them by SEM, Raman, XRD and UV-Vis spectroscopies. Besides, The as-prepared Au@Ag NP / filter paper is stable, uniform, and exhibits high efficiency and reusability in the catalytic reduction of 4-nitrophenol (4-NP) with a high rate constant as well as an average conversion of 96% in 3 minutes during 6 times. While acting as catalysts, the composites display excellent surface enhanced Raman scattering (SERS) efficiency, allowing the real-time monitoring of chemical reactions. The obtained Au@Ag bimetallic catalysts /filter paper show an exceptional superior catalytic activity for the reduction of 4-nitrophenol (4-NP) which can be completely reduced to 4-aminophenol (4-AP) in a single step within seconds., surpassing most reported noble metal nanoparticle catalysts. The results have not only paved the way for the wide application of Au@Ag NPs / filter paper but also provide a new road for the application of other bimetallic NPs

Figure 1
(Invited) Chemical Modulation of Precursor Reactivity for Systematic Synthesis of High-Quality Quantum Dots
Authors: Han Hee-Sun 
Published: July 2021

With their size-dependent electronic properties, solution processability, and synthetic tunability, quantum dots (QDs) have been heavily studied for fundamental research and commercial applications. The performance of QD-based devices and QD probes are greatly affected by their size distribution and structural quality. To date, extensive studies have investigated size control, allowing us to grow QDs with less than a single monolayer difference in their size variation. In contrast, high quality QD growth is achieved
mostly by trial-and-error and a reaction condition optimized for a specific QD system cannot be adapted to QDs of different sizes or materials. To address this critical gap in QD synthesis, we first identified the key parameters contributing to the structural quality of QDs and examined how they affect QD growth. Our study shows that the reaction temperature governs the instability of the QD surface and the level of lattice relaxation, while precursor reactivity impacts the reaction kinetics at the surface as well as satellite particle formation. Systematic optimization of growth conditions requires independent tuning of these two parameters. Unfortunately, conventional precursors do not allow temperature-independent modulation of their reactivity, requiring new precursors to be synthesized for different reaction conditions. This approach is inherently low throughput and labor-intensive.
To enable temperature-independent modulation of precursor reactivity, we designed a new sulfur precursor that has a reactivity that can be chemically tuned in a predictable manner. The additives used for this reaction are commercially available, making the new chemistry highly accessible. The new precursor chemistry
represents a new paradigm for QD growth: chemically-induced precursor conversion. We show that the new precursor allows systematic growth of high quality QDs of various sizes and materials for both core-only and core-shell QDs. Moreover, a close examination of the growth results provided invaluable insight into the nanoparticle growth process, enabling logical tuning of the reaction condition. We also show that this new precursor chemistry employs completely different reaction mechanisms for nucleation and growth. By effectively separating growth and nucleation even at a high concentration of precursors, the new precursor allows heat-up-based growth of high quality shells that are comparable to those created by the injection method. Our study inspires the design of other precursors to systematically grow high quality nanocrystals and achieve the heat-up-based growth of high quality shells, a highly economical and scalable approach for large-scale synthesis.
(Invited) Thermal Transport in Multi-component Nanoparticle Superstructures
Authors: Stahley James B , Zanjani Mehdi 
Published: July 2021

Engineering nanoscale structures and interfaces through self-assembly of quantum dots and nanoparticles is a powerful approach in material design and processing. Nanoscale building blocks, with a variety of shapes, sizes, and compositions, can self-assemble into different structures with desirable electronic, photonic, and phononic properties. Recent advances in synthesis and characterization of complex nano-scale units has provided a large library of multi-component building blocks that can significantly expand the current scope of colloidal self-assembly. However, the complexity of these building blocks makes it significantly harder to predict the resulting self-assembled structures and their transport properties. Therefore, developing predictive tools based on computational and machine learning approaches becomes a necessity for successful implementation of new material design procedures.
In this work, we utilize a combination of Molecular Dynamics simulations and Machine Learning-based approaches to investigate the self-assembly process of multi-component nanoparticles. We establish a computational framework to help identify feasible multi-component superstructures, and investigate various designs as a function of shape and size of the building blocks. Additionally, our approach transforms the high-dimensional energy landscapes of nanoparticles into low-dimensional parameter spaces by using Manifold Learning techniques. This illustration of the phase space compresses the full energy profile of the system into a single snapshot that contains important information regarding the assembly process and allows the comparison of the behavior of constructs with different geometric features in the same abstract space. This approach also facilitates the discovery of intuitive self-assembly pathways based on the energy footprint of nanoparticles.
Furthermore, we develop a computational framework to predict transport properties of the identified multi-component superstructures. Band structure calculations are performed to capture the influence of key parameters such as building block size/shape on the photonic and phononic properties of various structural designs. Finally, we present a Genetic Algorithm (GA)-based platform to implement an inverse material-design process where superstructures with desired transport properties are obtained from the available building blocks. Our results provide new insight into designing multi-component nano-scale structures and will help pave the way for developing powerful tools for material discovery and identification.
(Keynote) Electrochemistry Insights into the Development of Colloidal Quantum Dot Infrared Photodetectors
Authors: Guyot-Sionnest Philippe 
Published: July 2021

Colloidal quantum dots may provide a cheap and versatile approach to infrared detection and light sources. Indeed, advances in the past few years have led to commercial high definition shortwave infrared imaging with PbS quantum dots as well as academic demonstrations of solution processed mid-wave thermal imaging with HgTe quantum dots, dual band detection, flexible and hyperspectral sensors.
This talk will start by describing how this progress derived from insights gained by electrochemical studies of colloidal quantum dots. Following a path previously traced for conducting polymers, solution phase electrochemical reduction and spectroscopy allowed the first observation of redox stability in quantum dot states of the colloidal quantum dots. This then led to electrochemical studies in films which confirmed ambipolar stability in the lead chalcogenides. The next key electrochemistry contribution was the development of the “solid-state ligand exchange” which increased 1000-fold the charge transfer rates across quantum dots. This led to the first observation of ohmic conduction in films of quantum dots and subsequent studies of hopping transport in these materials, before photoconductivity studies and translation to solid state gating. Electrochemistry is still routinely used to extract mobility in a state resolved manner, while that has only been recently realized with solid state gating. Electrochemistry is easily coupled with spectroscopy, and this allows much information on the states and their related interband and intraband transitions. Electrochemistry and photoluminescence have even been applied to single dots microscopy in the visible, which was used to study blinking and its elimination under reducing conditions. Electrochemistry also allowed to lower the laser threshold of quantum dot films under reducing conditions and this approach is now being translated to solid state devices by others. Finally, controlled doping is essential for devices and this is another area where electrochemistry provides much insights.
This talk will then briefly describe the status of infrared photodetection beyond silicon with colloidal quantum dots, and emphasize the performance metrics and potential avenue for improvements.