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Bosten E, Pardon M, Chen K, Koppen V, Van Herck G, Hellings M, Cabooter D. Assisted Active Learning for Model-Based Method Development in Liquid Chromatography. Anal Chem 2024. [PMID: 38979746 DOI: 10.1021/acs.analchem.4c02700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
In recent decades, there has been a growing interest in fully automated methods for tackling complex optimization problems across various fields. Active learning (AL) and its variant, assisted active learning (AAL), incorporating guidance or assistance from external sources into the learning process, play key roles in this automation by enabling the autonomous selection of optimal experimental conditions to efficiently explore the problem space. These approaches are particularly valuable in situations wherein experimentation is costly or time-consuming. This study explores the application of AAL in model-based method development (MD) for liquid chromatography (LC) by using Bayesian statistics to incorporate historical data and analyte information for the generation of initial retention models. The process involves updating the model parameters based on new experiments, coupled with an active data selection method to choose the most informative experiment to run in a subsequent step. This iterative process balances model exploitation and experimental exploration until a satisfactory separation is achieved. The effectiveness of this approach is demonstrated via two practical examples, resulting in optimized separations in a limited number of experiments by optimizing the gradient slope. It is shown that the ability of AAL to leverage past knowledge and compound information to improve accuracy and reduce experimental runs offers a flexible alternative approach to fixed design methods.
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Affiliation(s)
- Emery Bosten
- Department for Pharmaceutical and Pharmacological Sciences, Pharmaceutical Analysis, University of Leuven (KU Leuven), Herestraat 49, 3000 Leuven, Belgium
- Therapeutics Development & Supply, Janssen Pharmaceutica, Turnhoutseweg 30, B-2340 Beerse, Belgium
| | - Marie Pardon
- Department for Pharmaceutical and Pharmacological Sciences, Pharmaceutical Analysis, University of Leuven (KU Leuven), Herestraat 49, 3000 Leuven, Belgium
| | - Kai Chen
- Therapeutics Development & Supply, Janssen Pharmaceutica, Turnhoutseweg 30, B-2340 Beerse, Belgium
| | - Valerie Koppen
- Therapeutics Development & Supply, Janssen Pharmaceutica, Turnhoutseweg 30, B-2340 Beerse, Belgium
| | - Gerd Van Herck
- Therapeutics Development & Supply, Janssen Pharmaceutica, Turnhoutseweg 30, B-2340 Beerse, Belgium
| | - Mario Hellings
- Therapeutics Development & Supply, Janssen Pharmaceutica, Turnhoutseweg 30, B-2340 Beerse, Belgium
| | - Deirdre Cabooter
- Department for Pharmaceutical and Pharmacological Sciences, Pharmaceutical Analysis, University of Leuven (KU Leuven), Herestraat 49, 3000 Leuven, Belgium
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2
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Wiczling P, Kamedulska A. Comparison of Chromatographic Stationary Phases Using a Bayesian-Based Multilevel Model. Anal Chem 2024; 96:1310-1319. [PMID: 38204188 DOI: 10.1021/acs.analchem.3c04697] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2024]
Abstract
Herein, we used a Bayesian multilevel model of chromatographic retention to compare five reversed-phase high-performance liquid chromatography stationary phases: XBridge Shield RP18, XTerra MS C18, XBridge Phenyl, XBridge C8, and Xterra MS C8. For this, we used a large data set of retention times collected using chromatographic techniques coupled with mass spectrometry. The experiments were conducted in gradient mode for an initial mixture of 300 small analytes for a wide range of pH values in methanol and acetonitrile at two temperatures and for three gradient durations. Our analysis was based on a mechanistic model derived from the principles and fundamentals of liquid chromatography and utilized previously reported chromatographic parameters. The data and model were used to characterize the between-column differences in the chromatographic parameters of the neutral, acidic, and basic analytes. The analysis provides an interpretable summary of stationary-phase properties that can be used in decision-making, i.e., finding the best chromatographic conditions using limited experimental data. The proposed approach is an interesting alternative to the existing approaches used to compare chromatographic stationary phases.
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Affiliation(s)
- Paweł Wiczling
- Department of Biopharmaceutics and Pharmacodynamics, Medical University of Gdańsk, Al. Gen. Hallera 107, 80-416 Gdańsk, Poland
| | - Agnieszka Kamedulska
- Department of Biopharmaceutics and Pharmacodynamics, Medical University of Gdańsk, Al. Gen. Hallera 107, 80-416 Gdańsk, Poland
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3
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Tang S, Zhang P, Gao M, Xiao Q, Li Z, Dong H, Tian Y, Xu F, Zhang Y. A chemical derivatization-based pseudotargeted LC-MS/MS method for high coverage determination of dipeptides. Anal Chim Acta 2023; 1274:341570. [PMID: 37455081 DOI: 10.1016/j.aca.2023.341570] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 06/04/2023] [Accepted: 06/27/2023] [Indexed: 07/18/2023]
Abstract
Dipeptides (DPs) have attracted more and more attention in many research fields due to their important biological functions and promising roles as disease biomarkers. However, the determination of DPs in biological samples is very challenging owing to the limited availability of commercial standards, high structure diversity, distinct physical and chemical characteristics, wide concentration range, and the extensive existence of isomers. In this study, a pseudotargeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) method coupled with chemical derivatization for the simultaneous analysis of 400 DPs and their constructing amino acids (AAs) in biospecimens is established. Dansyl chloride (Dns-Cl) chemical derivatization was introduced to provide characteristic MS fragments for annotation and improve the chromatographic separation of DP isomers. A retention time (RT) prediction model was constructed using 83 standards (63 DPs and 20 AAs) based on their quantitative structural retention relationship (QSRR) after the Dns-Cl labeling, which largely facilitated the annotation of the DPs without standards. Finally, we applied this method to investigate the profile change of DPs in a cisplatin-induced acute kidney injury (AKI) rat model. The established workflow provides a platform to profile DPs and expand our understanding of these little-studied metabolites.
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Affiliation(s)
- Shaoran Tang
- China Pharmaceutical University Nanjing Drum Tower Hospital, Nanjing, 210009, PR China; Key Laboratory of Drug Quality Control and Pharmacovigilance (Ministry of Education), China Pharmaceutical University, Nanjing, 210009, PR China
| | - Pei Zhang
- Key Laboratory of Drug Quality Control and Pharmacovigilance (Ministry of Education), China Pharmaceutical University, Nanjing, 210009, PR China
| | - Meiyu Gao
- Key Laboratory of Drug Quality Control and Pharmacovigilance (Ministry of Education), China Pharmaceutical University, Nanjing, 210009, PR China
| | - Qinwen Xiao
- Key Laboratory of Drug Quality Control and Pharmacovigilance (Ministry of Education), China Pharmaceutical University, Nanjing, 210009, PR China
| | - Zhaoqian Li
- Key Laboratory of Drug Quality Control and Pharmacovigilance (Ministry of Education), China Pharmaceutical University, Nanjing, 210009, PR China
| | - Haijuan Dong
- The Public Laboratory Platform, China Pharmaceutical University, Nanjing, 210009, PR China
| | - Yuan Tian
- Key Laboratory of Drug Quality Control and Pharmacovigilance (Ministry of Education), China Pharmaceutical University, Nanjing, 210009, PR China
| | - Fengguo Xu
- Key Laboratory of Drug Quality Control and Pharmacovigilance (Ministry of Education), China Pharmaceutical University, Nanjing, 210009, PR China.
| | - Yuxin Zhang
- China Pharmaceutical University Nanjing Drum Tower Hospital, Nanjing, 210009, PR China.
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Kumari P, Van Laethem T, Hubert P, Fillet M, Sacré PY, Hubert C. Quantitative Structure Retention-Relationship Modeling: Towards an Innovative General-Purpose Strategy. Molecules 2023; 28:molecules28041696. [PMID: 36838689 PMCID: PMC9964055 DOI: 10.3390/molecules28041696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 02/05/2023] [Accepted: 02/08/2023] [Indexed: 02/12/2023] Open
Abstract
Reversed-Phase Liquid Chromatography (RPLC) is a common liquid chromatographic mode used for the control of pharmaceutical compounds during their drug life cycle. Nevertheless, determining the optimal chromatographic conditions that enable this separation is time consuming and requires a lot of lab work. Quantitative Structure Retention Relationship models (QSRR) are helpful for doing this job with minimal time and cost expenditures by predicting retention times of known compounds without performing experiments. In the current work, several QSRR models were built and compared for their adequacy in predicting the retention times. The regression models were based on a combination of linear and non-linear algorithms such as Multiple Linear Regression, Support Vector Regression, Least Absolute Shrinkage and Selection Operator, Random Forest, and Gradient Boosted Regression. Models were built for five pH conditions, i.e., at pH 2.7, 3.5, 6.5, and 8.0. In the end, the model predictions were combined using stacking and the performances of all models were compared. The k-nearest neighbor-based application domain filter was established to assess the reliability of the prediction for further compound prioritization. Altogether, this study can be insightful for analytical chemists working with RPLC to begin with the computational prediction modeling such as QSRR to predict the separation of small molecules.
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Affiliation(s)
- Priyanka Kumari
- Department of Pharmacy, Laboratory of Pharmaceutical Analytical Chemistry, University of Liège (ULiege), CIRM, Quartier Hopital (B36 Tower 4), Avenue Hippocrate, 4000 Liège, Belgium
- Laboratory for the Analysis of Medicines, University of Liège (ULiege), CIRM, Quartier Hopital (B36 Tower 4), Avenue Hippocrate, 4000 Liège, Belgium
- Correspondence: (P.K.); (C.H.); Tel.: +32-(0)-43664326 (C.H.)
| | - Thomas Van Laethem
- Department of Pharmacy, Laboratory of Pharmaceutical Analytical Chemistry, University of Liège (ULiege), CIRM, Quartier Hopital (B36 Tower 4), Avenue Hippocrate, 4000 Liège, Belgium
- Laboratory for the Analysis of Medicines, University of Liège (ULiege), CIRM, Quartier Hopital (B36 Tower 4), Avenue Hippocrate, 4000 Liège, Belgium
| | - Philippe Hubert
- Department of Pharmacy, Laboratory of Pharmaceutical Analytical Chemistry, University of Liège (ULiege), CIRM, Quartier Hopital (B36 Tower 4), Avenue Hippocrate, 4000 Liège, Belgium
| | - Marianne Fillet
- Laboratory for the Analysis of Medicines, University of Liège (ULiege), CIRM, Quartier Hopital (B36 Tower 4), Avenue Hippocrate, 4000 Liège, Belgium
| | - Pierre-Yves Sacré
- Department of Pharmacy, Laboratory of Pharmaceutical Analytical Chemistry, University of Liège (ULiege), CIRM, Quartier Hopital (B36 Tower 4), Avenue Hippocrate, 4000 Liège, Belgium
| | - Cédric Hubert
- Department of Pharmacy, Laboratory of Pharmaceutical Analytical Chemistry, University of Liège (ULiege), CIRM, Quartier Hopital (B36 Tower 4), Avenue Hippocrate, 4000 Liège, Belgium
- Correspondence: (P.K.); (C.H.); Tel.: +32-(0)-43664326 (C.H.)
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Kamedulska A, Kubik Ł, Jacyna J, Struck-Lewicka W, Markuszewski MJ, Wiczling P. Toward the General Mechanistic Model of Liquid Chromatographic Retention. Anal Chem 2022; 94:11070-11080. [PMID: 35903961 PMCID: PMC9756959 DOI: 10.1021/acs.analchem.2c02034] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Large datasets of chromatographic retention times are relatively easy to collect. This statement is particularly true when mixtures of compounds are analyzed under a series of gradient conditions using chromatographic techniques coupled with mass spectrometry detection. Such datasets carry much information about chromatographic retention that, if extracted, can provide useful predictive information. In this work, we proposed a mechanistic model that jointly explains the relationship between pH, organic modifier type, temperature, gradient duration, and analyte retention based on liquid chromatography retention data collected for 187 small molecules. The model was built utilizing a Bayesian multilevel framework. The model assumes (i) a deterministic Neue equation that describes the relationship between retention time and analyte-specific and instrument-specific parameters, (ii) the relationship between analyte-specific descriptors (log P, pKa, and functional groups) and analyte-specific chromatographic parameters, and (iii) stochastic components of between-analyte and residual variability. The model utilizes prior knowledge about model parameters to regularize predictions which is important as there is ample information about the retention behavior of analytes in various stationary phases in the literature. The usefulness of the proposed model in providing interpretable summaries of complex data and in decision making is discussed.
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Affiliation(s)
- Agnieszka Kamedulska
- Department of Biopharmaceutics and Pharmacodynamics, Medical University of Gdańsk, Al. Gen. Hallera 107, 80-416 Gdańsk, Poland
| | - Łukasz Kubik
- Department of Biopharmaceutics and Pharmacodynamics, Medical University of Gdańsk, Al. Gen. Hallera 107, 80-416 Gdańsk, Poland
| | - Julia Jacyna
- Department of Biopharmaceutics and Pharmacodynamics, Medical University of Gdańsk, Al. Gen. Hallera 107, 80-416 Gdańsk, Poland
| | - Wiktoria Struck-Lewicka
- Department of Biopharmaceutics and Pharmacodynamics, Medical University of Gdańsk, Al. Gen. Hallera 107, 80-416 Gdańsk, Poland
| | - Michał J Markuszewski
- Department of Biopharmaceutics and Pharmacodynamics, Medical University of Gdańsk, Al. Gen. Hallera 107, 80-416 Gdańsk, Poland
| | - Paweł Wiczling
- Department of Biopharmaceutics and Pharmacodynamics, Medical University of Gdańsk, Al. Gen. Hallera 107, 80-416 Gdańsk, Poland
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6
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Wang M, Xu XY, Wang HD, Wang HM, Liu MY, Hu WD, Chen BX, Jiang MT, Qi J, Li XH, Yang WZ, Gao XM. A multi-dimensional liquid chromatography/high-resolution mass spectrometry approach combined with computational data processing for the comprehensive characterization of the multicomponents from Cuscuta chinensis. J Chromatogr A 2022; 1675:463162. [DOI: 10.1016/j.chroma.2022.463162] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 05/17/2022] [Accepted: 05/18/2022] [Indexed: 02/07/2023]
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7
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Kamedulska A, Kubik Ł, Wiczling P. Statistical analysis of isocratic chromatographic data using Bayesian modeling. Anal Bioanal Chem 2022; 414:3471-3481. [DOI: 10.1007/s00216-022-03968-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 01/28/2022] [Accepted: 02/08/2022] [Indexed: 11/01/2022]
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Ciura K, Kovačević S, Pastewska M, Kapica H, Kornela M, Sawicki W. Prediction of the chromatographic hydrophobicity index with immobilized artificial membrane chromatography using simple molecular descriptors and artificial neural networks. J Chromatogr A 2021; 1660:462666. [PMID: 34781046 DOI: 10.1016/j.chroma.2021.462666] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 10/27/2021] [Accepted: 10/28/2021] [Indexed: 12/01/2022]
Abstract
Screening of physicochemical properties should be considered one of the essential steps in the drug discovery pipeline. Among the available methods, biomimetic chromatography with an immobilized artificial membrane is a powerful tool for simulating interactions between a molecule and a biological membrane. This study developed a quantitative structure-retention relationships model that would predict the chromatographically determined affinity of xenobiotics to phospholipids, expressed as a chromatographic hydrophobicity index determined using immobilized artificial membrane chromatography. A heterogeneous set of 261 molecules, mostly showing pharmacological activity or toxicity, was analyzed chromatographically to realize this goal. The chromatographic analysis was performed using the fast gradient protocol proposed by Valko, where acetonitrile was applied as an organic modifier. Next, quantitative structure-retention relationships modeling was performed using multiple linear regression (MLR) methods and artificial neural networks (ANNs) coupled with genetic algorithm (GA)-inspired selection. Subsequently, the selection of the best ANN was supported by statistical parameters, the sum of ranking differences approach with the comparison of rank by random numbers and hierarchical cluster analysis.
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Affiliation(s)
- Krzesimir Ciura
- Department of Physical Chemistry, Medical University of Gdańsk, Aleja Gen. Hallera 107, Gdańsk 80-416, Poland; QSAR Lab Ltd., Trzy Lipy 3St. Gdańsk 80-172, Poland.
| | - Strahinja Kovačević
- Department of Applied and Engineering Chemistry, Faculty of Technology Novi Sad, University of Novi Sad, Bulevar cara Lazara 1, Novi Sad 21000, Serbia
| | - Monika Pastewska
- Department of Physical Chemistry, Medical University of Gdańsk, Aleja Gen. Hallera 107, Gdańsk 80-416, Poland
| | - Hanna Kapica
- Department of Physical Chemistry, Medical University of Gdańsk, Aleja Gen. Hallera 107, Gdańsk 80-416, Poland
| | - Martyna Kornela
- Department of Physical Chemistry, Medical University of Gdańsk, Aleja Gen. Hallera 107, Gdańsk 80-416, Poland
| | - Wiesław Sawicki
- Department of Physical Chemistry, Medical University of Gdańsk, Aleja Gen. Hallera 107, Gdańsk 80-416, Poland
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9
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Jirayupat C, Nagashima K, Hosomi T, Takahashi T, Tanaka W, Samransuksamer B, Zhang G, Liu J, Kanai M, Yanagida T. Image Processing and Machine Learning for Automated Identification of Chemo-/Biomarkers in Chromatography-Mass Spectrometry. Anal Chem 2021; 93:14708-14715. [PMID: 34704450 DOI: 10.1021/acs.analchem.1c03163] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
We present a method named NPFimg, which automatically identifies multivariate chemo-/biomarker features of analytes in chromatography-mass spectrometry (MS) data by combining image processing and machine learning. NPFimg processes a two-dimensional MS map (m/z vs retention time) to discriminate analytes and identify and visualize the marker features. Our approach allows us to comprehensively characterize the signals in MS data without the conventional peak picking process, which suffers from false peak detections. The feasibility of marker identification is successfully demonstrated in case studies of aroma odor and human breath on gas chromatography-mass spectrometry (GC-MS) even at the parts per billion level. Comparison with the widely used XCMS shows the excellent reliability of NPFimg, in that it has lower error rates of signal acquisition and marker identification. In addition, we show the potential applicability of NPFimg to the untargeted metabolomics of human breath. While this study shows the limited applications, NPFimg is potentially applicable to data processing in diverse metabolomics/chemometrics using GC-MS and liquid chromatography-MS. NPFimg is available as open source on GitHub (http://github.com/poomcj/NPFimg) under the MIT license.
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Affiliation(s)
- Chaiyanut Jirayupat
- Department of Applied Chemistry, Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan.,Interdisciplinary Graduate School of Engineering Sciences, Kyushu University, 6-1 Kasuga-Koen, Kasuga, Fukuoka 816-8580, Japan
| | - Kazuki Nagashima
- Department of Applied Chemistry, Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan.,Japan Science and Technology Agency (JST), PRESTO, 4-1-8 Honcho, Kawaguchi, Saitama 332-0012, Japan
| | - Takuro Hosomi
- Department of Applied Chemistry, Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan.,Japan Science and Technology Agency (JST), PRESTO, 4-1-8 Honcho, Kawaguchi, Saitama 332-0012, Japan
| | - Tsunaki Takahashi
- Department of Applied Chemistry, Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan.,Japan Science and Technology Agency (JST), PRESTO, 4-1-8 Honcho, Kawaguchi, Saitama 332-0012, Japan
| | - Wataru Tanaka
- Department of Applied Chemistry, Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
| | - Benjarong Samransuksamer
- Department of Applied Chemistry, Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
| | - Guozhu Zhang
- Department of Applied Chemistry, Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
| | - Jiangyang Liu
- Department of Applied Chemistry, Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
| | - Masaki Kanai
- Institute for Materials Chemistry and Engineering, Kyushu University, 6-1 Kasuga-Koen, Kasuga, Fukuoka 816-8580, Japan
| | - Takeshi Yanagida
- Department of Applied Chemistry, Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan.,Interdisciplinary Graduate School of Engineering Sciences, Kyushu University, 6-1 Kasuga-Koen, Kasuga, Fukuoka 816-8580, Japan.,Institute for Materials Chemistry and Engineering, Kyushu University, 6-1 Kasuga-Koen, Kasuga, Fukuoka 816-8580, Japan
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