Bayesian optimization of radical polymerization reactions in a flow synthesis system

Proportions of monomers in a copolymer will greatly affect the properties of materials. However, due to a phenomenon known as composition drift, the proportions of monomers in a copolymer can deviate from the value expected from the raw monomer ratio because of differences in monomer reactivity. It...

Full description

Saved in:

Bibliographic Details
Published in:	Science and technology of advanced materials. Methods
Main Authors:	Takasuka, Shogo, Ito, Sho, Oikawa, Shunto, Harashima, Yosuke, Takayama, Tomoaki, Nag, Aniruddha, Wakiuchi, Araki, Ando, Tsuyoshi, Sugawara, Tetsunori, Hatanaka, Miho, Miyao, Tomoyuki, Matsubara, Takamitsu, Ohnishi, Yu-Ya, Ajiro, Hiroharu, Fujii, Mikiya
Format:	Journal Article
Language:	English
Published:	Taylor & Francis Group 13-11-2024
Subjects:	bayesian optimization flow synthesis methyl methacrylate Polymer radical polymerization Styrene
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Abstract	Proportions of monomers in a copolymer will greatly affect the properties of materials. However, due to a phenomenon known as composition drift, the proportions of monomers in a copolymer can deviate from the value expected from the raw monomer ratio because of differences in monomer reactivity. It is therefore necessary to optimize the polymerization process to account for such composition drift. In the present study, styrene-methyl methacrylate copolymers were generated using a flow synthesis system and the processing variables were tuned employing Bayesian optimization (BO) to obtain a target composition. First trials of BO with generation of four candidate points per cycle, completed the optimization within five cycles. Subsequent Bayesian Optimization (BO) trial, using 40 points per cycle, identified several sets of processing conditions that could achieve the desired copolymer composition, accompanied by variations in other physical properties. To optimize the monomer composition ratio in the polymer, it was discovered from a data science perspective that the solvent-to-monomer ratio was as crucial as the styrene proportions. The role of each variable in the radical polymerization reaction was elucidated by assessing the extensive array of processing conditions while evaluating several broad trends. The proposed model confirms that specific monomer proportions can be produced in a copolymer using machine learning while investigating the reaction mechanism. In the future, the use of multi-objective BO to fine-tune the processing conditions is expected to allow optimization of the copolymer composition together with adjustment of physical properties.
AbstractList	Proportions of monomers in a copolymer will greatly affect the properties of materials. However, due to a phenomenon known as composition drift, the proportions of monomers in a copolymer can deviate from the value expected from the raw monomer ratio because of differences in monomer reactivity. It is therefore necessary to optimize the polymerization process to account for such composition drift. In the present study, styrene-methyl methacrylate copolymers were generated using a flow synthesis system and the processing variables were tuned employing Bayesian optimization (BO) to obtain a target composition. First trials of BO with generation of four candidate points per cycle, completed the optimization within five cycles. Subsequent Bayesian Optimization (BO) trial, using 40 points per cycle, identified several sets of processing conditions that could achieve the desired copolymer composition, accompanied by variations in other physical properties. To optimize the monomer composition ratio in the polymer, it was discovered from a data science perspective that the solvent-to-monomer ratio was as crucial as the styrene proportions. The role of each variable in the radical polymerization reaction was elucidated by assessing the extensive array of processing conditions while evaluating several broad trends. The proposed model confirms that specific monomer proportions can be produced in a copolymer using machine learning while investigating the reaction mechanism. In the future, the use of multi-objective BO to fine-tune the processing conditions is expected to allow optimization of the copolymer composition together with adjustment of physical properties.
Author	Sugawara, Tetsunori Miyao, Tomoyuki Oikawa, Shunto Wakiuchi, Araki Matsubara, Takamitsu Hatanaka, Miho Takayama, Tomoaki Ito, Sho Fujii, Mikiya Takasuka, Shogo Nag, Aniruddha Harashima, Yosuke Ando, Tsuyoshi Ohnishi, Yu-Ya Ajiro, Hiroharu
Author_xml	– sequence: 1 givenname: Shogo orcidid: 0000-0002-4601-2432 surname: Takasuka fullname: Takasuka, Shogo organization: Graduate School of Science and Technology, Nara Institute of Science and Technology, Takayama-cho, Ikoma, Japan – sequence: 2 givenname: Sho surname: Ito fullname: Ito, Sho organization: Graduate School of Science and Technology, Nara Institute of Science and Technology, Takayama-cho, Ikoma, Japan – sequence: 3 givenname: Shunto surname: Oikawa fullname: Oikawa, Shunto organization: Graduate School of Science and Technology, Nara Institute of Science and Technology, Takayama-cho, Ikoma, Japan – sequence: 4 givenname: Yosuke surname: Harashima fullname: Harashima, Yosuke organization: Graduate School of Science and Technology, Nara Institute of Science and Technology, Takayama-cho, Ikoma, Japan, Data Science Center, Nara Institute of Science and Technology, Takayama-cho, Ikoma, Japan – sequence: 5 givenname: Tomoaki surname: Takayama fullname: Takayama, Tomoaki organization: Graduate School of Science and Technology, Nara Institute of Science and Technology, Takayama-cho, Ikoma, Japan, Data Science Center, Nara Institute of Science and Technology, Takayama-cho, Ikoma, Japan – sequence: 6 givenname: Aniruddha surname: Nag fullname: Nag, Aniruddha organization: Graduate School of Science and Technology, Nara Institute of Science and Technology, Takayama-cho, Ikoma, Japan – sequence: 7 givenname: Araki surname: Wakiuchi fullname: Wakiuchi, Araki organization: Graduate School of Science and Technology, Nara Institute of Science and Technology, Takayama-cho, Ikoma, Japan, Materials Informatics Initiative, RD technology and digital transformation center, JSR Corporation, Kawasaki-ku, Kawasaki, Kanagawa, Japan – sequence: 8 givenname: Tsuyoshi surname: Ando fullname: Ando, Tsuyoshi organization: Graduate School of Science and Technology, Nara Institute of Science and Technology, Takayama-cho, Ikoma, Japan – sequence: 9 givenname: Tetsunori surname: Sugawara fullname: Sugawara, Tetsunori organization: Fine Chemical Process Dept, JSR Corporation, Yokkaichi, Japan – sequence: 10 givenname: Miho surname: Hatanaka fullname: Hatanaka, Miho organization: Department of Chemistry, Faculty of Science and Technology, Keio University, Yokohama, Kanagawa, Japan – sequence: 11 givenname: Tomoyuki surname: Miyao fullname: Miyao, Tomoyuki organization: Graduate School of Science and Technology, Nara Institute of Science and Technology, Takayama-cho, Ikoma, Japan, Data Science Center, Nara Institute of Science and Technology, Takayama-cho, Ikoma, Japan, Center for Material Research Platform, Nara Institute of Science and Technology, Takayama-cho, Ikoma, Nara, Japan – sequence: 12 givenname: Takamitsu surname: Matsubara fullname: Matsubara, Takamitsu organization: Graduate School of Science and Technology, Nara Institute of Science and Technology, Takayama-cho, Ikoma, Japan – sequence: 13 givenname: Yu-Ya surname: Ohnishi fullname: Ohnishi, Yu-Ya organization: Materials Informatics Initiative, RD technology and digital transformation center, JSR Corporation, Kawasaki-ku, Kawasaki, Kanagawa, Japan – sequence: 14 givenname: Hiroharu surname: Ajiro fullname: Ajiro, Hiroharu organization: Graduate School of Science and Technology, Nara Institute of Science and Technology, Takayama-cho, Ikoma, Japan, Data Science Center, Nara Institute of Science and Technology, Takayama-cho, Ikoma, Japan, Center for Material Research Platform, Nara Institute of Science and Technology, Takayama-cho, Ikoma, Nara, Japan – sequence: 15 givenname: Mikiya orcidid: 0000-0002-3728-3097 surname: Fujii fullname: Fujii, Mikiya organization: Data Science Center, Nara Institute of Science and Technology, Takayama-cho, Ikoma, Japan, Center for Material Research Platform, Nara Institute of Science and Technology, Takayama-cho, Ikoma, Nara, Japan
BookMark	eNpNkMtOwzAQRS1UJErpJyDlB1rGdhwnS6h4VKrEpqytiR_gKokrOxIKX09CKWI1d-5ozuJck1kXOkvILYU1hRLumCwKyAHWDFi-ZjkTVJYXZD71q-kw-5evyDKlAwCwUnLGYU72DzjY5LHLwrH3rf_C3odxcVlE4zU22TE0Q2vj-RIt6imkzHcZZq4Jn1kauv5jpKQxpd62N-TSYZPs8ncuyNvT437zstq9Pm8397uVpqIqV7wwtuZaSGGF5rXgJYfaMOsA0TijLRdOlDJnhlMnJM0LaTlnBQJFlKD5gmxPXBPwoI7RtxgHFdCrnyLEd4Wx97qxSsjxubDgDMe8cnVlcl0IW1EsqDY5jCxxYukYUorW_fEoqMm0OptWk2n1a5p_A5u-c9Q
Cites_doi	10.1021/acspolymersau.1c00050 10.3390/polym10010103 10.1063/5.0087392 10.1038/s41598-022-05784-w 10.1002/cjoc.202100544 10.1016/j.commatsci.2021.110815 10.1016/j.commatsci.2020.110244 10.1073/pnas.2106042118 10.1080/27660400.2022.2123263 10.1007/s11426-020-9969-y 10.1016/S0014-3057(01)00242-7 10.1016/j.polymer.2022.124577 10.1021/acsami.1c24715 10.1016/j.cej.2022.138443 10.1021/acscentsci.2c00207 10.1021/acsomega.2c04919 10.1038/s41428-018-0165-0 10.1002/ange.202214511 10.1214/aos/1013203451 10.1002/app.12161 10.1021/acs.oprd.5b00210 10.1016/j.nimb.2021.11.014 10.1021/acscentsci.3c00050 10.1002/macp.202300039 10.1016/j.actamat.2022.117751 10.1016/j.patter.2021.100238 10.1002/app.35234 10.1021/acsmacrolett.9b00933 10.1080/1023666X.2021.2004012 10.1002/advs.202200164 10.1021/acs.macromol.7b01890 10.1002/anie.202308838 10.1021/acs.macromol.1c00728 10.1017/9781108348973 10.1002/ange.201810384 10.1021/acs.jcim.1c01031 10.1039/C7RE00063D 10.1016/j.commatsci.2019.109286 10.1016/j.eurpolymj.2019.109225 10.1039/d2py00040g 10.1016/j.isci.2021.102781 10.1109/JPROC.2015.2494218 10.26434/chemrxiv-2022-tlz53 10.3390/electronicmat3020017 10.1021/acs.analchem.1c04585 10.1021/acsami.1c23610 10.1038/s41524-022-00859-8 10.1039/D3PY01372C 10.1038/s41467-020-16874-6 10.3762/bjoc.18.182 10.1039/D2DD00144F 10.1021/acsomega.2c06008 10.1039/c9py00982e 10.1002/mame.202200626 10.1021/acsnano.1c07298 10.1039/D2SC02839E 10.1021/ma980294x 10.1021/acsabm.2c00346 10.1039/D2RE00054G 10.1016/j.slasd.2022.01.002 10.1021/acs.analchem.1c04224 10.48550/arXiv.1910.06403 10.1021/acsapm.0c00921 10.1021/jacs.1c08181 10.1016/j.progpolymsci.2020.101256 10.1002/mats.1993.040020313 10.1016/j.commatsci.2022.111417 10.1016/j.mtcomm.2022.103440 10.1021/acs.macromol.9b00846
ContentType	Journal Article
DBID	AAYXX CITATION DOA
DOI	10.1080/27660400.2024.2425178
DatabaseName	CrossRef Directory of Open Access Journals
DatabaseTitle	CrossRef
DatabaseTitleList
Database_xml	– sequence: 1 dbid: DOA name: Directory of Open Access Journals url: http://www.doaj.org/ sourceTypes: Open Website
DeliveryMethod	fulltext_linktorsrc
Discipline	Engineering
EISSN	2766-0400
ExternalDocumentID	oai_doaj_org_article_5767e6e0fd3a49fb9d4c65e91a61cd40 10_1080_27660400_2024_2425178
GroupedDBID	0YH AAYXX ALMA_UNASSIGNED_HOLDINGS CITATION EBS GROUPED_DOAJ M~E TDBHL
ID	FETCH-LOGICAL-c1598-36deb3c575e5c3b53830bd2ef0aadfdce35f58742d31f571467e3326a01aa70c3
IEDL.DBID	DOA
ISSN	2766-0400
IngestDate	Mon Nov 18 19:22:22 EST 2024 Wed Nov 20 13:22:59 EST 2024
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Language	English
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c1598-36deb3c575e5c3b53830bd2ef0aadfdce35f58742d31f571467e3326a01aa70c3
ORCID	0000-0002-3728-3097 0000-0002-4601-2432
OpenAccessLink	https://doaj.org/article/5767e6e0fd3a49fb9d4c65e91a61cd40
ParticipantIDs	doaj_primary_oai_doaj_org_article_5767e6e0fd3a49fb9d4c65e91a61cd40 crossref_primary_10_1080_27660400_2024_2425178
PublicationCentury	2000
PublicationDate	2024-11-13
PublicationDateYYYYMMDD	2024-11-13
PublicationDate_xml	– month: 11 year: 2024 text: 2024-11-13 day: 13
PublicationDecade	2020
PublicationTitle	Science and technology of advanced materials. Methods
PublicationYear	2024
Publisher	Taylor & Francis Group
Publisher_xml	– name: Taylor & Francis Group
References	e_1_3_4_3_1 e_1_3_4_61_1 e_1_3_4_9_1 e_1_3_4_42_1 e_1_3_4_7_1 e_1_3_4_40_1 e_1_3_4_5_1 e_1_3_4_23_1 e_1_3_4_46_1 e_1_3_4_69_1 e_1_3_4_21_1 e_1_3_4_44_1 e_1_3_4_27_1 e_1_3_4_65_1 e_1_3_4_25_1 e_1_3_4_48_1 e_1_3_4_67_1 e_1_3_4_29_1 e_1_3_4_53_1 e_1_3_4_30_1 e_1_3_4_51_1 e_1_3_4_70_1 e_1_3_4_13_1 e_1_3_4_34_1 e_1_3_4_59_1 e_1_3_4_55_1 e_1_3_4_11_1 e_1_3_4_32_1 e_1_3_4_17_1 e_1_3_4_38_1 e_1_3_4_15_1 e_1_3_4_36_1 e_1_3_4_57_1 e_1_3_4_19_1 e_1_3_4_4_1 e_1_3_4_2_1 e_1_3_4_64_1 e_1_3_4_8_1 e_1_3_4_20_1 e_1_3_4_41_1 e_1_3_4_6_1 e_1_3_4_60_1 e_1_3_4_24_1 e_1_3_4_45_1 e_1_3_4_22_1 e_1_3_4_43_1 e_1_3_4_28_1 e_1_3_4_49_1 e_1_3_4_66_1 e_1_3_4_26_1 e_1_3_4_47_1 e_1_3_4_68_1 Brandrup J (e_1_3_4_63_1) 1999 Garnett R. (e_1_3_4_62_1) 2023 e_1_3_4_31_1 e_1_3_4_52_1 e_1_3_4_50_1 e_1_3_4_71_1 e_1_3_4_12_1 e_1_3_4_35_1 e_1_3_4_58_1 e_1_3_4_10_1 e_1_3_4_33_1 e_1_3_4_54_1 e_1_3_4_16_1 e_1_3_4_39_1 e_1_3_4_14_1 e_1_3_4_37_1 e_1_3_4_56_1 e_1_3_4_18_1
References_xml	– volume-title: Polymer Handbook Fourth Edition year: 1999 ident: e_1_3_4_63_1 contributor: fullname: Brandrup J – ident: e_1_3_4_25_1 doi: 10.1021/acspolymersau.1c00050 – ident: e_1_3_4_21_1 doi: 10.3390/polym10010103 – ident: e_1_3_4_23_1 doi: 10.1063/5.0087392 – ident: e_1_3_4_32_1 doi: 10.1038/s41598-022-05784-w – ident: e_1_3_4_45_1 doi: 10.1002/cjoc.202100544 – ident: e_1_3_4_15_1 doi: 10.1016/j.commatsci.2021.110815 – ident: e_1_3_4_16_1 doi: 10.1016/j.commatsci.2020.110244 – ident: e_1_3_4_37_1 doi: 10.1073/pnas.2106042118 – ident: e_1_3_4_71_1 doi: 10.1080/27660400.2022.2123263 – ident: e_1_3_4_54_1 doi: 10.1007/s11426-020-9969-y – ident: e_1_3_4_9_1 doi: 10.1016/S0014-3057(01)00242-7 – ident: e_1_3_4_11_1 doi: 10.1016/j.polymer.2022.124577 – ident: e_1_3_4_17_1 doi: 10.1021/acsami.1c24715 – ident: e_1_3_4_69_1 doi: 10.1016/j.cej.2022.138443 – ident: e_1_3_4_40_1 doi: 10.1021/acscentsci.2c00207 – ident: e_1_3_4_67_1 doi: 10.1021/acsomega.2c04919 – ident: e_1_3_4_3_1 doi: 10.1038/s41428-018-0165-0 – ident: e_1_3_4_57_1 doi: 10.1002/ange.202214511 – ident: e_1_3_4_66_1 doi: 10.1214/aos/1013203451 – ident: e_1_3_4_7_1 doi: 10.1002/app.12161 – ident: e_1_3_4_58_1 doi: 10.1021/acs.oprd.5b00210 – ident: e_1_3_4_30_1 doi: 10.1016/j.nimb.2021.11.014 – ident: e_1_3_4_56_1 doi: 10.1021/acscentsci.3c00050 – ident: e_1_3_4_55_1 doi: 10.1002/macp.202300039 – ident: e_1_3_4_10_1 doi: 10.1016/j.actamat.2022.117751 – ident: e_1_3_4_24_1 doi: 10.1016/j.patter.2021.100238 – ident: e_1_3_4_8_1 doi: 10.1002/app.35234 – ident: e_1_3_4_48_1 doi: 10.1021/acsmacrolett.9b00933 – ident: e_1_3_4_4_1 doi: 10.1080/1023666X.2021.2004012 – ident: e_1_3_4_12_1 doi: 10.1002/advs.202200164 – ident: e_1_3_4_43_1 doi: 10.1021/acs.macromol.7b01890 – ident: e_1_3_4_46_1 doi: 10.1002/anie.202308838 – ident: e_1_3_4_14_1 doi: 10.1021/acs.macromol.1c00728 – volume-title: Bayesian Optimization year: 2023 ident: e_1_3_4_62_1 doi: 10.1017/9781108348973 contributor: fullname: Garnett R. – ident: e_1_3_4_47_1 doi: 10.1002/ange.201810384 – ident: e_1_3_4_22_1 doi: 10.1021/acs.jcim.1c01031 – ident: e_1_3_4_35_1 doi: 10.1039/C7RE00063D – ident: e_1_3_4_18_1 doi: 10.1016/j.commatsci.2019.109286 – ident: e_1_3_4_2_1 doi: 10.1016/j.eurpolymj.2019.109225 – ident: e_1_3_4_59_1 doi: 10.1039/d2py00040g – ident: e_1_3_4_27_1 doi: 10.1016/j.isci.2021.102781 – ident: e_1_3_4_31_1 doi: 10.1109/JPROC.2015.2494218 – ident: e_1_3_4_53_1 doi: 10.26434/chemrxiv-2022-tlz53 – ident: e_1_3_4_68_1 doi: 10.3390/electronicmat3020017 – ident: e_1_3_4_38_1 doi: 10.1021/acs.analchem.1c04585 – ident: e_1_3_4_13_1 doi: 10.1021/acsami.1c23610 – ident: e_1_3_4_26_1 doi: 10.1038/s41524-022-00859-8 – ident: e_1_3_4_64_1 doi: 10.1039/D3PY01372C – ident: e_1_3_4_51_1 doi: 10.1038/s41467-020-16874-6 – ident: e_1_3_4_36_1 doi: 10.3762/bjoc.18.182 – ident: e_1_3_4_61_1 doi: 10.1039/D2DD00144F – ident: e_1_3_4_42_1 doi: 10.1021/acsomega.2c06008 – ident: e_1_3_4_49_1 doi: 10.1039/c9py00982e – ident: e_1_3_4_60_1 doi: 10.1002/mame.202200626 – ident: e_1_3_4_28_1 doi: 10.1021/acsnano.1c07298 – ident: e_1_3_4_19_1 doi: 10.1039/D2SC02839E – ident: e_1_3_4_5_1 doi: 10.1021/ma980294x – ident: e_1_3_4_41_1 doi: 10.1021/acsabm.2c00346 – ident: e_1_3_4_34_1 doi: 10.1039/D2RE00054G – ident: e_1_3_4_33_1 doi: 10.1016/j.slasd.2022.01.002 – ident: e_1_3_4_39_1 doi: 10.1021/acs.analchem.1c04224 – ident: e_1_3_4_65_1 doi: 10.48550/arXiv.1910.06403 – ident: e_1_3_4_20_1 doi: 10.1021/acsapm.0c00921 – ident: e_1_3_4_52_1 doi: 10.1021/jacs.1c08181 – ident: e_1_3_4_50_1 doi: 10.1016/j.progpolymsci.2020.101256 – ident: e_1_3_4_6_1 doi: 10.1002/mats.1993.040020313 – ident: e_1_3_4_29_1 doi: 10.1016/j.commatsci.2022.111417 – ident: e_1_3_4_70_1 doi: 10.1016/j.mtcomm.2022.103440 – ident: e_1_3_4_44_1 doi: 10.1021/acs.macromol.9b00846
SSID	ssj0002873230
Score	2.3246546
Snippet	Proportions of monomers in a copolymer will greatly affect the properties of materials. However, due to a phenomenon known as composition drift, the...
SourceID	doaj crossref
SourceType	Open Website Aggregation Database
SubjectTerms	bayesian optimization flow synthesis methyl methacrylate Polymer radical polymerization Styrene
Title	Bayesian optimization of radical polymerization reactions in a flow synthesis system
URI	https://doaj.org/article/5767e6e0fd3a49fb9d4c65e91a61cd40
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
link	http://sdu.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV09T8MwELWgEwyIT1G-5IE1JY4T2x0ptGJioUhs0cUfUlFJqoYK9d9zl6RVNxbWxImsd479np17x9i9FB5C6nxkAlgqYaYjAGGjYKwCJOQw9M3WxZt-_TDPY7LJ2Zb6on_CWnvgFjgU7Ep75ePgJD4biqFLrcr8UIAS1qWtWo_Vjpj6bLaMtERyvUnZMfFDopWiAYuSMEkHRLQFlVbbWYx2PPubxWVyzI46Vsgf296csD1fnrLDHa_AMzYdwdpTxiOv8Cv_6tIneRX4EpqzFr6o5ms6f-nuIBtschZqPis58DCvfni9LpHw1bOatw7O5-x9Mp4-vURdSYTIIu8wkVQO1a9FjuUzKwucrWRcuMSHGMAFZ73MQmZQ7jopQqZpGvQSGRrEAkDHVl6wXlmV_pLxkKaZKYS3jtpDMEpAkoSgDfnFuKTPBhts8kXrfJGLzlB0A2ZOYOYdmH02IgS3jcm4urmA4cy7cOZ_hfPqP15yzQ6oY5QyKOQN630vV_6W7dduddcMk1-HDMFn
link.rule.ids	315,782,786,866,2106,27933,27934
linkProvider	Directory of Open Access Journals
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Bayesian+optimization+of+radical+polymerization+reactions+in+a+flow+synthesis+system&rft.jtitle=Science+and+technology+of+advanced+materials.+Methods&rft.au=Takasuka%2C+Shogo&rft.au=Ito%2C+Sho&rft.au=Oikawa%2C+Shunto&rft.au=Harashima%2C+Yosuke&rft.date=2024-11-13&rft.issn=2766-0400&rft.eissn=2766-0400&rft_id=info:doi/10.1080%2F27660400.2024.2425178&rft.externalDBID=n%2Fa&rft.externalDocID=10_1080_27660400_2024_2425178
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2766-0400&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2766-0400&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2766-0400&client=summon