Bring Chemical Intuition to Chips: Transferable Chemical-intuitive Model to Predict Photophysics of Organic Aggregates Article Swipe
Junyi Gong
,
Ben Zhong Tang
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.26434/chemrxiv-2022-r4bfq-v3
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.26434/chemrxiv-2022-r4bfq-v3
While machine-learning methods indicated good adaptability for machine-learning algorithms in the pan-chemistry field by its breakthroughs in pharmacy. Materials research still benefits from such new techniques fewer due to the inconsistency in the paradigm of study in the diversely different subareas which demand special treatment individually. In this contribution, we proposed an innovative design of the embedding method, which is inspired by chemical intuition, to bring neural networks into the field for modelling photophysics of the organic light-emitting materials in condensed states. We outline this framework and demonstrate its successful implementation in the predictive classification of fluorophores by its mechanisms, direction of spectra shift from solution to solid-state, and regression of spectral features, including emission peaks wavelengths in pristine solid-state and nano-aggregates. Our work may serve as an example for a specific area of materials research to transfer the empirical chemical intuition into machine-learning models and build comprehensive performance-oriented pre-screening systems to develop new compounds with demanded characters.
Related Topics
Concepts
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.26434/chemrxiv-2022-r4bfq-v3
- https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/6275b81c59f0d64d0086f5ea/original/bring-chemical-intuition-to-chips-transferable-chemical-intuitive-model-to-predict-photophysics-of-organic-aggregates.pdf
- OA Status
- gold
- References
- 44
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4280505298
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4280505298Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.26434/chemrxiv-2022-r4bfq-v3Digital Object Identifier
- Title
-
Bring Chemical Intuition to Chips: Transferable Chemical-intuitive Model to Predict Photophysics of Organic AggregatesWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-05-09Full publication date if available
- Authors
-
Junyi Gong, Ben Zhong TangList of authors in order
- Landing page
-
https://doi.org/10.26434/chemrxiv-2022-r4bfq-v3Publisher landing page
- PDF URL
-
https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/6275b81c59f0d64d0086f5ea/original/bring-chemical-intuition-to-chips-transferable-chemical-intuitive-model-to-predict-photophysics-of-organic-aggregates.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/6275b81c59f0d64d0086f5ea/original/bring-chemical-intuition-to-chips-transferable-chemical-intuitive-model-to-predict-photophysics-of-organic-aggregates.pdfDirect OA link when available
- Concepts
-
Intuition, Computer science, Adaptability, Artificial intelligence, Machine learning, Biochemical engineering, Cognitive science, Engineering, Biology, Psychology, EcologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
44Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4280505298 |
|---|---|
| doi | https://doi.org/10.26434/chemrxiv-2022-r4bfq-v3 |
| ids.doi | https://doi.org/10.26434/chemrxiv-2022-r4bfq-v3 |
| ids.openalex | https://openalex.org/W4280505298 |
| fwci | 0.0 |
| type | preprint |
| title | Bring Chemical Intuition to Chips: Transferable Chemical-intuitive Model to Predict Photophysics of Organic Aggregates |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T10977 |
| topics[0].field.id | https://openalex.org/fields/27 |
| topics[0].field.display_name | Medicine |
| topics[0].score | 0.9817000031471252 |
| topics[0].domain.id | https://openalex.org/domains/4 |
| topics[0].domain.display_name | Health Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2741 |
| topics[0].subfield.display_name | Radiology, Nuclear Medicine and Imaging |
| topics[0].display_name | Optical Imaging and Spectroscopy Techniques |
| topics[1].id | https://openalex.org/T10211 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9657999873161316 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1703 |
| topics[1].subfield.display_name | Computational Theory and Mathematics |
| topics[1].display_name | Computational Drug Discovery Methods |
| topics[2].id | https://openalex.org/T11948 |
| topics[2].field.id | https://openalex.org/fields/25 |
| topics[2].field.display_name | Materials Science |
| topics[2].score | 0.960099995136261 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2505 |
| topics[2].subfield.display_name | Materials Chemistry |
| topics[2].display_name | Machine Learning in Materials Science |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C132010649 |
| concepts[0].level | 2 |
| concepts[0].score | 0.8744634389877319 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q189222 |
| concepts[0].display_name | Intuition |
| concepts[1].id | https://openalex.org/C41008148 |
| concepts[1].level | 0 |
| concepts[1].score | 0.5984265804290771 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[1].display_name | Computer science |
| concepts[2].id | https://openalex.org/C177606310 |
| concepts[2].level | 2 |
| concepts[2].score | 0.5265812277793884 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q5674297 |
| concepts[2].display_name | Adaptability |
| concepts[3].id | https://openalex.org/C154945302 |
| concepts[3].level | 1 |
| concepts[3].score | 0.5047096014022827 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[3].display_name | Artificial intelligence |
| concepts[4].id | https://openalex.org/C119857082 |
| concepts[4].level | 1 |
| concepts[4].score | 0.4896714985370636 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[4].display_name | Machine learning |
| concepts[5].id | https://openalex.org/C183696295 |
| concepts[5].level | 1 |
| concepts[5].score | 0.32199588418006897 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q2487696 |
| concepts[5].display_name | Biochemical engineering |
| concepts[6].id | https://openalex.org/C188147891 |
| concepts[6].level | 1 |
| concepts[6].score | 0.17350253462791443 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q147638 |
| concepts[6].display_name | Cognitive science |
| concepts[7].id | https://openalex.org/C127413603 |
| concepts[7].level | 0 |
| concepts[7].score | 0.15486791729927063 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q11023 |
| concepts[7].display_name | Engineering |
| concepts[8].id | https://openalex.org/C86803240 |
| concepts[8].level | 0 |
| concepts[8].score | 0.0 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q420 |
| concepts[8].display_name | Biology |
| concepts[9].id | https://openalex.org/C15744967 |
| concepts[9].level | 0 |
| concepts[9].score | 0.0 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q9418 |
| concepts[9].display_name | Psychology |
| concepts[10].id | https://openalex.org/C18903297 |
| concepts[10].level | 1 |
| concepts[10].score | 0.0 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q7150 |
| concepts[10].display_name | Ecology |
| keywords[0].id | https://openalex.org/keywords/intuition |
| keywords[0].score | 0.8744634389877319 |
| keywords[0].display_name | Intuition |
| keywords[1].id | https://openalex.org/keywords/computer-science |
| keywords[1].score | 0.5984265804290771 |
| keywords[1].display_name | Computer science |
| keywords[2].id | https://openalex.org/keywords/adaptability |
| keywords[2].score | 0.5265812277793884 |
| keywords[2].display_name | Adaptability |
| keywords[3].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[3].score | 0.5047096014022827 |
| keywords[3].display_name | Artificial intelligence |
| keywords[4].id | https://openalex.org/keywords/machine-learning |
| keywords[4].score | 0.4896714985370636 |
| keywords[4].display_name | Machine learning |
| keywords[5].id | https://openalex.org/keywords/biochemical-engineering |
| keywords[5].score | 0.32199588418006897 |
| keywords[5].display_name | Biochemical engineering |
| keywords[6].id | https://openalex.org/keywords/cognitive-science |
| keywords[6].score | 0.17350253462791443 |
| keywords[6].display_name | Cognitive science |
| keywords[7].id | https://openalex.org/keywords/engineering |
| keywords[7].score | 0.15486791729927063 |
| keywords[7].display_name | Engineering |
| language | en |
| locations[0].id | doi:10.26434/chemrxiv-2022-r4bfq-v3 |
| locations[0].is_oa | True |
| locations[0].source | |
| locations[0].license | cc-by-nc-nd |
| locations[0].pdf_url | https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/6275b81c59f0d64d0086f5ea/original/bring-chemical-intuition-to-chips-transferable-chemical-intuitive-model-to-predict-photophysics-of-organic-aggregates.pdf |
| locations[0].version | acceptedVersion |
| locations[0].raw_type | posted-content |
| locations[0].license_id | https://openalex.org/licenses/cc-by-nc-nd |
| locations[0].is_accepted | True |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | https://doi.org/10.26434/chemrxiv-2022-r4bfq-v3 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5021250755 |
| authorships[0].author.orcid | https://orcid.org/0000-0001-7592-7132 |
| authorships[0].author.display_name | Junyi Gong |
| authorships[0].countries | CN |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I4210116924 |
| authorships[0].affiliations[0].raw_affiliation_string | Chinese University of Hong Kong, Shenzhen |
| authorships[0].institutions[0].id | https://openalex.org/I4210116924 |
| authorships[0].institutions[0].ror | https://ror.org/02d5ks197 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I177725633, https://openalex.org/I180726961, https://openalex.org/I4210116924 |
| authorships[0].institutions[0].country_code | CN |
| authorships[0].institutions[0].display_name | Chinese University of Hong Kong, Shenzhen |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Junyi Gong |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Chinese University of Hong Kong, Shenzhen |
| authorships[1].author.id | https://openalex.org/A5089379373 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-0293-964X |
| authorships[1].author.display_name | Ben Zhong Tang |
| authorships[1].countries | CN |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I180726961 |
| authorships[1].affiliations[0].raw_affiliation_string | Shenzhen University |
| authorships[1].affiliations[1].institution_ids | https://openalex.org/I4210116924 |
| authorships[1].affiliations[1].raw_affiliation_string | Chinese University of Hong Kong, Shenzhen |
| authorships[1].institutions[0].id | https://openalex.org/I4210116924 |
| authorships[1].institutions[0].ror | https://ror.org/02d5ks197 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I177725633, https://openalex.org/I180726961, https://openalex.org/I4210116924 |
| authorships[1].institutions[0].country_code | CN |
| authorships[1].institutions[0].display_name | Chinese University of Hong Kong, Shenzhen |
| authorships[1].institutions[1].id | https://openalex.org/I180726961 |
| authorships[1].institutions[1].ror | https://ror.org/01vy4gh70 |
| authorships[1].institutions[1].type | education |
| authorships[1].institutions[1].lineage | https://openalex.org/I180726961 |
| authorships[1].institutions[1].country_code | CN |
| authorships[1].institutions[1].display_name | Shenzhen University |
| authorships[1].author_position | last |
| authorships[1].raw_author_name | Ben Zhong Tang |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Chinese University of Hong Kong, Shenzhen, Shenzhen University |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/6275b81c59f0d64d0086f5ea/original/bring-chemical-intuition-to-chips-transferable-chemical-intuitive-model-to-predict-photophysics-of-organic-aggregates.pdf |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Bring Chemical Intuition to Chips: Transferable Chemical-intuitive Model to Predict Photophysics of Organic Aggregates |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10977 |
| primary_topic.field.id | https://openalex.org/fields/27 |
| primary_topic.field.display_name | Medicine |
| primary_topic.score | 0.9817000031471252 |
| primary_topic.domain.id | https://openalex.org/domains/4 |
| primary_topic.domain.display_name | Health Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2741 |
| primary_topic.subfield.display_name | Radiology, Nuclear Medicine and Imaging |
| primary_topic.display_name | Optical Imaging and Spectroscopy Techniques |
| related_works | https://openalex.org/W2807945054, https://openalex.org/W2961085424, https://openalex.org/W4306674287, https://openalex.org/W4224009465, https://openalex.org/W2391412735, https://openalex.org/W2129834593, https://openalex.org/W3112261659, https://openalex.org/W3190889427, https://openalex.org/W1963934847, https://openalex.org/W4206493799 |
| cited_by_count | 0 |
| locations_count | 1 |
| best_oa_location.id | doi:10.26434/chemrxiv-2022-r4bfq-v3 |
| best_oa_location.is_oa | True |
| best_oa_location.source | |
| best_oa_location.license | cc-by-nc-nd |
| best_oa_location.pdf_url | https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/6275b81c59f0d64d0086f5ea/original/bring-chemical-intuition-to-chips-transferable-chemical-intuitive-model-to-predict-photophysics-of-organic-aggregates.pdf |
| best_oa_location.version | acceptedVersion |
| best_oa_location.raw_type | posted-content |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by-nc-nd |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | https://doi.org/10.26434/chemrxiv-2022-r4bfq-v3 |
| primary_location.id | doi:10.26434/chemrxiv-2022-r4bfq-v3 |
| primary_location.is_oa | True |
| primary_location.source | |
| primary_location.license | cc-by-nc-nd |
| primary_location.pdf_url | https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/6275b81c59f0d64d0086f5ea/original/bring-chemical-intuition-to-chips-transferable-chemical-intuitive-model-to-predict-photophysics-of-organic-aggregates.pdf |
| primary_location.version | acceptedVersion |
| primary_location.raw_type | posted-content |
| primary_location.license_id | https://openalex.org/licenses/cc-by-nc-nd |
| primary_location.is_accepted | True |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | https://doi.org/10.26434/chemrxiv-2022-r4bfq-v3 |
| publication_date | 2022-05-09 |
| publication_year | 2022 |
| referenced_works | https://openalex.org/W6682524060, https://openalex.org/W1999798000, https://openalex.org/W2189911347, https://openalex.org/W3031157367, https://openalex.org/W2791253051, https://openalex.org/W2883583109, https://openalex.org/W2157531738, https://openalex.org/W2111547563, https://openalex.org/W2080635178, https://openalex.org/W2114704115, https://openalex.org/W3105362409, https://openalex.org/W2086729168, https://openalex.org/W1618905105, https://openalex.org/W1678356000, https://openalex.org/W2970971581, https://openalex.org/W1513123914, https://openalex.org/W1984990803, https://openalex.org/W2795265078, https://openalex.org/W3126869842, https://openalex.org/W3042973234, https://openalex.org/W3037427133, https://openalex.org/W2324258662, https://openalex.org/W2095074059, https://openalex.org/W2174991771, https://openalex.org/W1605923270, https://openalex.org/W2058220696, https://openalex.org/W2200017991, https://openalex.org/W2021748110, https://openalex.org/W2171268876, https://openalex.org/W1522301498, https://openalex.org/W2158698691, https://openalex.org/W2919231240, https://openalex.org/W2767147442, https://openalex.org/W2026498919, https://openalex.org/W4252898064, https://openalex.org/W2955314574, https://openalex.org/W4301384416, https://openalex.org/W2177603935, https://openalex.org/W2110324274, https://openalex.org/W4241826337, https://openalex.org/W4295312788, https://openalex.org/W2491458126, https://openalex.org/W2150981663, https://openalex.org/W2102866064 |
| referenced_works_count | 44 |
| abstract_inverted_index.a | 130 |
| abstract_inverted_index.In | 46 |
| abstract_inverted_index.We | 82 |
| abstract_inverted_index.an | 51, 127 |
| abstract_inverted_index.as | 126 |
| abstract_inverted_index.by | 13, 61, 97 |
| abstract_inverted_index.in | 9, 16, 31, 36, 79, 91, 117 |
| abstract_inverted_index.is | 59 |
| abstract_inverted_index.of | 34, 54, 74, 95, 101, 110, 133 |
| abstract_inverted_index.to | 28, 64, 106, 136, 151 |
| abstract_inverted_index.we | 49 |
| abstract_inverted_index.Our | 122 |
| abstract_inverted_index.and | 86, 108, 120, 145 |
| abstract_inverted_index.due | 27 |
| abstract_inverted_index.for | 6, 71, 129 |
| abstract_inverted_index.its | 14, 88, 98 |
| abstract_inverted_index.may | 124 |
| abstract_inverted_index.new | 24, 153 |
| abstract_inverted_index.the | 10, 29, 32, 37, 55, 69, 75, 92, 138 |
| abstract_inverted_index.area | 132 |
| abstract_inverted_index.from | 22, 104 |
| abstract_inverted_index.good | 4 |
| abstract_inverted_index.into | 68, 142 |
| abstract_inverted_index.such | 23 |
| abstract_inverted_index.this | 47, 84 |
| abstract_inverted_index.with | 155 |
| abstract_inverted_index.work | 123 |
| abstract_inverted_index.While | 0 |
| abstract_inverted_index.bring | 65 |
| abstract_inverted_index.build | 146 |
| abstract_inverted_index.fewer | 26 |
| abstract_inverted_index.field | 12, 70 |
| abstract_inverted_index.peaks | 115 |
| abstract_inverted_index.serve | 125 |
| abstract_inverted_index.shift | 103 |
| abstract_inverted_index.still | 20 |
| abstract_inverted_index.study | 35 |
| abstract_inverted_index.which | 41, 58 |
| abstract_inverted_index.demand | 42 |
| abstract_inverted_index.design | 53 |
| abstract_inverted_index.models | 144 |
| abstract_inverted_index.neural | 66 |
| abstract_inverted_index.develop | 152 |
| abstract_inverted_index.example | 128 |
| abstract_inverted_index.method, | 57 |
| abstract_inverted_index.methods | 2 |
| abstract_inverted_index.organic | 76 |
| abstract_inverted_index.outline | 83 |
| abstract_inverted_index.special | 43 |
| abstract_inverted_index.spectra | 102 |
| abstract_inverted_index.states. | 81 |
| abstract_inverted_index.systems | 150 |
| abstract_inverted_index.benefits | 21 |
| abstract_inverted_index.chemical | 62, 140 |
| abstract_inverted_index.demanded | 156 |
| abstract_inverted_index.emission | 114 |
| abstract_inverted_index.inspired | 60 |
| abstract_inverted_index.networks | 67 |
| abstract_inverted_index.paradigm | 33 |
| abstract_inverted_index.pristine | 118 |
| abstract_inverted_index.proposed | 50 |
| abstract_inverted_index.research | 19, 135 |
| abstract_inverted_index.solution | 105 |
| abstract_inverted_index.specific | 131 |
| abstract_inverted_index.spectral | 111 |
| abstract_inverted_index.subareas | 40 |
| abstract_inverted_index.transfer | 137 |
| abstract_inverted_index.Materials | 18 |
| abstract_inverted_index.compounds | 154 |
| abstract_inverted_index.condensed | 80 |
| abstract_inverted_index.different | 39 |
| abstract_inverted_index.direction | 100 |
| abstract_inverted_index.diversely | 38 |
| abstract_inverted_index.embedding | 56 |
| abstract_inverted_index.empirical | 139 |
| abstract_inverted_index.features, | 112 |
| abstract_inverted_index.framework | 85 |
| abstract_inverted_index.including | 113 |
| abstract_inverted_index.indicated | 3 |
| abstract_inverted_index.intuition | 141 |
| abstract_inverted_index.materials | 78, 134 |
| abstract_inverted_index.modelling | 72 |
| abstract_inverted_index.pharmacy. | 17 |
| abstract_inverted_index.treatment | 44 |
| abstract_inverted_index.algorithms | 8 |
| abstract_inverted_index.innovative | 52 |
| abstract_inverted_index.intuition, | 63 |
| abstract_inverted_index.predictive | 93 |
| abstract_inverted_index.regression | 109 |
| abstract_inverted_index.successful | 89 |
| abstract_inverted_index.techniques | 25 |
| abstract_inverted_index.characters. | 157 |
| abstract_inverted_index.demonstrate | 87 |
| abstract_inverted_index.mechanisms, | 99 |
| abstract_inverted_index.solid-state | 119 |
| abstract_inverted_index.wavelengths | 116 |
| abstract_inverted_index.adaptability | 5 |
| abstract_inverted_index.fluorophores | 96 |
| abstract_inverted_index.photophysics | 73 |
| abstract_inverted_index.solid-state, | 107 |
| abstract_inverted_index.breakthroughs | 15 |
| abstract_inverted_index.comprehensive | 147 |
| abstract_inverted_index.contribution, | 48 |
| abstract_inverted_index.inconsistency | 30 |
| abstract_inverted_index.individually. | 45 |
| abstract_inverted_index.pan-chemistry | 11 |
| abstract_inverted_index.pre-screening | 149 |
| abstract_inverted_index.classification | 94 |
| abstract_inverted_index.implementation | 90 |
| abstract_inverted_index.light-emitting | 77 |
| abstract_inverted_index.machine-learning | 1, 7, 143 |
| abstract_inverted_index.nano-aggregates. | 121 |
| abstract_inverted_index.performance-oriented | 148 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.5 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile.value | 0.0761737 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |