Treffer: Enabling data-driven design of block copolymer self-assembly.
Title:
Enabling data-driven design of block copolymer self-assembly.
Authors:
Magosso C; Department of Electronics and Telecommunications, Politecnico di Torino, 10129, Turin, Italy.; Advanced Materials Metrology and Life Sciences Division, INRiM (Istituto Nazionale di Ricerca Metrologica), 10135, Turin, Italy., Murataj I; Advanced Materials Metrology and Life Sciences Division, INRiM (Istituto Nazionale di Ricerca Metrologica), 10135, Turin, Italy., Perego M; CNR-IMM, Unit of Agrate Brianza, Via C. Olivetti 2, I-20864, Agrate Brianza, Italy., Seguini G; CNR-IMM, Unit of Agrate Brianza, Via C. Olivetti 2, I-20864, Agrate Brianza, Italy., Audus DJ; Materials Science and Engineering Division, National Institute of Standards and Technology, Gaithersburg, Maryland, 20899, USA., Milano G; Advanced Materials Metrology and Life Sciences Division, INRiM (Istituto Nazionale di Ricerca Metrologica), 10135, Turin, Italy. g.milano@inrim.it., Ferrarese Lupi F; Advanced Materials Metrology and Life Sciences Division, INRiM (Istituto Nazionale di Ricerca Metrologica), 10135, Turin, Italy. f.ferrareselupi@inrim.it.
Source:
Scientific data [Sci Data] 2025 Jun 21; Vol. 12 (1), pp. 1055. Date of Electronic Publication: 2025 Jun 21.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101640192 Publication Model: Electronic Cited Medium: Internet ISSN: 2052-4463 (Electronic) Linking ISSN: 20524463 NLM ISO Abbreviation: Sci Data Subsets: PubMed not MEDLINE; MEDLINE
Imprint Name(s):
Original Publication: London : Nature Publishing Group, 2014-
References:
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Bates, C. M. & Bates, F. S. 50th Anniversary Perspective: Block Polymers—Pure Potential. Macromolecules 50, 3–22 (2017). (PMID: 10.1021/acs.macromol.6b02355)
Yang, G. G. et al. Intelligent block copolymer self-assembly towards IoT hardware components. Nat. Rev. Electr. Eng. 1, 124–138 (2024). (PMID: 10.1038/s44287-024-00017-w)
Liu, C.-C. et al. Directed self-assembly of block copolymers for 7 nanometre FinFET technology and beyond. Nat. Electron. 1, 562–569 (2018). (PMID: 10.1038/s41928-018-0147-4)
Kim, J. Y. et al. Highly tunable refractive index visible-light metasurface from block copolymer self-assembly. Nat. Commun. 7, 12911 (2016). (PMID: 10.1038/ncomms12911276830775056414)
Wang, Z., Chan, C. L. C., Zhao, T. H., Parker, R. M. & Vignolini, S. Recent Advances in Block Copolymer Self-Assembly for the Fabrication of Photonic Films and Pigments. Adv. Opt. Mater. 9, 2100519 (2021). (PMID: 10.1002/adom.202100519)
Murataj, I. et al. Hyperbolic Metamaterials via Hierarchical Block Copolymer Nanostructures. Adv. Opt. Mater. 9, 2001933 (2021). (PMID: 10.1002/adom.202001933)
Pula, P. et al. Block Copolymer-Templated, Single-Step Synthesis of Transition Metal Oxide Nanostructures for Sensing Applications. ACS Appl. Mater. Interfaces 15, 57970–57980 (2023). (PMID: 10.1021/acsami.3c104393764461610739603)
Kim, T. H. et al. Flexible biomimetic block copolymer composite for temperature and long-wave infrared sensing. Sci. Adv. 9, eade0423 (2023). (PMID: 10.1126/sciadv.ade0423367636529916982)
Kang, Y., Walish, J. J., Gorishnyy, T. & Thomas, E. L. Broad-wavelength-range chemically tunable block-copolymer photonic gels. Nat. Mater. 6, 957–960 (2007). (PMID: 10.1038/nmat203217952084)
Cabral, H., Miyata, K., Osada, K. & Kataoka, K. Block Copolymer Micelles in Nanomedicine Applications. Chem. Rev. 118, 6844–6892 (2018). (PMID: 10.1021/acs.chemrev.8b0019929957926)
Materials Genome Initiative Strategic Plan. https://www.mgi.gov/sites/mgi/files/MGI-2021-Strategic-Plan.pdf .
Audus, D. J. & de Pablo, J. J. Polymer Informatics: Opportunities and Challenges. ACS Macro Lett. 6, 1078–1082 (2017). (PMID: 10.1021/acsmacrolett.7b00228292015355702941)
Eitouni, H. B. & Balsara, N. P. Thermodynamics of polymer blends. in Physical Properties of Polymers Handbook 339–356 (Springer, 2007).
Mark, J. E. Physical Properties of Polymers Handbook. vol. 1076 (Springer, 2007).
Tchoua, R. B. et al. Blending Education and Polymer Science: Semiautomated Creation of a Thermodynamic Property Database. J. Chem. Educ. 93, 1561–1568 (2016). (PMID: 10.1021/acs.jchemed.5b01032277955745082748)
Polymer Property Predictor and Database. https://pppdb.uchicago.edu/ .
Van Krevelen, D. W. & Te Nijenhuis, K. Chapter 8 - Interfacial Energy Properties. in Properties of Polymers (Fourth Edition) 229–244 https://doi.org/10.1016/B978-0-08-054819-7.00008-X (Elsevier, Amsterdam, 2009).
Grulke, E. A. Solubility Parameter Values. in Polymer Handbook 2336 pages (Brandrup, J., Immergut, E. H., Grulke, E. A. (Editor), 2003).
Small, P. A. Some factors affecting the solubility of polymers. J. Appl. Chem. 3, 71–80 (1953). (PMID: 10.1002/jctb.5010030205)
Hoy, K. L. New values of solubility parameters from vapor pressure data. Journal of Paint Technology 42, 76 (1970).
Matsen, M. W. & Schick, M. Stable and unstable phases of a diblock copolymer melt. Phys. Rev. Lett. 72, 2660–2663 (1994). (PMID: 10.1103/PhysRevLett.72.266010055940)
Bates, F. S. & Fredrickson, G. H. Block Copolymers—Designer Soft Materials. Phys. Today 52, 32–38 (1999). (PMID: 10.1063/1.882522)
Rebello, N. J. et al. The Block Copolymer Phase Behavior Database. J. Chem. Inf. Model. https://doi.org/10.1021/acs.jcim.4c00242 (2024).
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Arora, A. et al. Random Forest Predictor for Diblock Copolymer Phase Behavior. ACS Macro Lett. 10, 1339–1345 (2021). (PMID: 10.1021/acsmacrolett.1c0052135549019)
Hutzschenreuter, D. et al. SmartCom Digital System of Units (D-SI) Guide for the use of the metadata-format used in metrology for the easy-to-use, safe, harmonised and unambiguous digital transfer of metrological data (2019).
Gillespie, A. Materials by Design. NIST https://www.nist.gov/feature-stories/materials-design (2019).
Ferrarese Lupi, F. et al. Tailored and Guided Dewetting of Block Copolymer/Homopolymer Blends. Macromolecules 53, 7207–7217 (2020). (PMID: 10.1021/acs.macromol.0c01126)
Ferrarese Lupi, F. et al. Fine Tuning of Lithographic Masks through Thin Films of PS-b-PMMA with Different Molar Mass by Rapid Thermal Processing. ACS Appl. Mater. Interfaces 6, 7180–7188 (2014). (PMID: 10.1021/am500307424738855)
Murataj, I. et al. Artificial fingerprints engraved through block-copolymers as nanoscale physical unclonable functions for authentication and identification. Nat. Commun. 15, 10576 (2024). (PMID: 10.1038/s41467-024-54492-83966336911634899)
Ferrarese Lupi, F. et al. Flash grafting of functional random copolymers for surface neutralization. J. Mater. Chem. C 2, 4909–4917 (2014). (PMID: 10.1039/C4TC00328D)
Magosso, C. et al. Enabling data-driven design of block copolymer self-assembly - Database. https://doi.org/10.5281/zenodo.13927939 .
Tseng, Y.-C. & Darling, S. B. Block Copolymer Nanostructures for Technology. Polymers 2, 470–489 (2010). (PMID: 10.3390/polym2040470)
Giammaria, T. J. et al. Micrometer-Scale Ordering of Silicon-Containing Block Copolymer Thin Films via High-Temperature Thermal Treatments. ACS Appl. Mater. Interfaces 8, 9897–9908 (2016). (PMID: 10.1021/acsami.6b0230027020526)
Aprile, G. et al. Toward Lateral Length Standards at the Nanoscale Based on Diblock Copolymers. ACS Appl. Mater. Interfaces 9, 15685–15697 (2017). (PMID: 10.1021/acsami.7b0050928397488)
ChiaraMagosso. ChiaraMagosso/BlockMetrology: BlockMetrology - code. Zenodo https://doi.org/10.5281/zenodo.15430176 (2025).
ChiaraMagosso. ChiaraMagosso/BlockMetrology. GitHub https://github.com/ChiaraMagosso/BlockMetrology .
ADAblock/ADAblock.ijm at ijMacro_updates_only · ChiaraMagosso/ADAblock. GitHub https://github.com/ChiaraMagosso/ADAblock/blob/ijMacro_updates_only/ADAblock.ijm .
Hong, L., Wan, Y. & Jain, A. Fingerprint image enhancement: algorithm and performance evaluation. IEEE Trans. Pattern Anal. Mach. Intell. 20, 777–789 (1998). (PMID: 10.1109/34.709565)
fingerprint-enhancer · PyPI. https://pypi.org/project/fingerprint-enhancer/ .
Murphy, J. N., Harris, K. D. & Buriak, J. M. Automated Defect and Correlation Length Analysis of Block Copolymer Thin Film Nanopatterns. PLOS ONE 10, e0133088 (2015). (PMID: 10.1371/journal.pone.0133088262079904514826)
Tu, K.-H. et al. Machine Learning Predictions of Block Copolymer Self-Assembly. Adv. Mater. 32, 2005713 (2020). (PMID: 10.1002/adma.202005713)
Ferrarese Lupi, F. et al. Rapid thermal processing of self-assembling block copolymer thin films. Nanotechnology 24, 315601 (2013). (PMID: 10.1088/0957-4484/24/31/31560123851718)
Ceresoli, M. et al. Evolution of lateral ordering in symmetric block copolymer thin films upon rapid thermal processing. Nanotechnology 25, 275601 (2014). (PMID: 10.1088/0957-4484/25/27/27560124960172)
Sparnacci, K. et al. Thermal Stability of Functional P(S-r-MMA) Random Copolymers for Nanolithographic Applications. ACS Appl. Mater. Interfaces 7, 3920–3930 (2015). (PMID: 10.1021/am509088s25664773)
Ceresoli, M. et al. Scaling of correlation length in lamellae forming PS-b-PMMA thin films upon high temperature rapid thermal treatments. J. Mater. Chem. C 3, 8618–8624 (2015). (PMID: 10.1039/C5TC01473E)
Ceresoli, M. et al. Neutral wetting brush layers for block copolymer thin films using homopolymer blends processed at high temperatures. Nanotechnology 26, 415603 (2015). (PMID: 10.1088/0957-4484/26/41/41560326404164)
Semenov, A. N. Contribution to the theory of microphase layering in block-copolymer melts. Zh Eksp Teor Fiz 88, 1242–1256 (1985).
Murataj, I. et al. Hybrid Metrology for Nanostructured Optical Metasurfaces. ACS Appl. Mater. Interfaces 15, 57992–58002 (2023). (PMID: 10.1021/acsami.3c139233799146010739581)
Yee, T. D., Watson, C. L., Roehling, J. D., Han, T. Y. & Hiszpanski, A. M. Fabrication and 3D tomographic characterization of nanowire arrays and meshes with tunable dimensions from shear-aligned block copolymers. Soft Matter 15, 4898–4904 (2019). (PMID: 10.1039/C9SM00303G31166358)
Lynd, N. A., Meuler, A. J. & Hillmyer, M. A. Polydispersity and block copolymer self-assembly. Prog. Polym. Sci. 33, 875–893 (2008). (PMID: 10.1016/j.progpolymsci.2008.07.003)
Doise, J. et al. Strategies for Increasing the Rate of Defect Annihilation in the Directed Self-Assembly of High-χ Block Copolymers. ACS Appl. Mater. Interfaces 11, 48419–48427 (2019). (PMID: 10.1021/acsami.9b1785831752485)
Wernecke, J. et al. Traceable GISAXS measurements for pitch determination of a 25 nm self-assembled polymer grating. J. Appl. Crystallogr. 47, 1912–1920 (2014). (PMID: 10.1107/S1600576714021050)
Versailles Project on Advanced Materials and Standards (VAMAS). http://www.vamas.org/ ; http://www.vamas.org/twa0/documents/2024_vamas_twa0_p3_DBC.pdf .
Wilkinson, M. D. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci. Data 3, 160018 (2016). (PMID: 10.1038/sdata.2016.18269782444792175)
Grootveld, M. & Gürdal, G. OpenAIRE Guide on how to find a trustworthy repository for your data. https://doi.org/10.5281/zenodo.4077212 (2018).
Bates, C. M. & Bates, F. S. 50th Anniversary Perspective: Block Polymers—Pure Potential. Macromolecules 50, 3–22 (2017). (PMID: 10.1021/acs.macromol.6b02355)
Yang, G. G. et al. Intelligent block copolymer self-assembly towards IoT hardware components. Nat. Rev. Electr. Eng. 1, 124–138 (2024). (PMID: 10.1038/s44287-024-00017-w)
Liu, C.-C. et al. Directed self-assembly of block copolymers for 7 nanometre FinFET technology and beyond. Nat. Electron. 1, 562–569 (2018). (PMID: 10.1038/s41928-018-0147-4)
Kim, J. Y. et al. Highly tunable refractive index visible-light metasurface from block copolymer self-assembly. Nat. Commun. 7, 12911 (2016). (PMID: 10.1038/ncomms12911276830775056414)
Wang, Z., Chan, C. L. C., Zhao, T. H., Parker, R. M. & Vignolini, S. Recent Advances in Block Copolymer Self-Assembly for the Fabrication of Photonic Films and Pigments. Adv. Opt. Mater. 9, 2100519 (2021). (PMID: 10.1002/adom.202100519)
Murataj, I. et al. Hyperbolic Metamaterials via Hierarchical Block Copolymer Nanostructures. Adv. Opt. Mater. 9, 2001933 (2021). (PMID: 10.1002/adom.202001933)
Pula, P. et al. Block Copolymer-Templated, Single-Step Synthesis of Transition Metal Oxide Nanostructures for Sensing Applications. ACS Appl. Mater. Interfaces 15, 57970–57980 (2023). (PMID: 10.1021/acsami.3c104393764461610739603)
Kim, T. H. et al. Flexible biomimetic block copolymer composite for temperature and long-wave infrared sensing. Sci. Adv. 9, eade0423 (2023). (PMID: 10.1126/sciadv.ade0423367636529916982)
Kang, Y., Walish, J. J., Gorishnyy, T. & Thomas, E. L. Broad-wavelength-range chemically tunable block-copolymer photonic gels. Nat. Mater. 6, 957–960 (2007). (PMID: 10.1038/nmat203217952084)
Cabral, H., Miyata, K., Osada, K. & Kataoka, K. Block Copolymer Micelles in Nanomedicine Applications. Chem. Rev. 118, 6844–6892 (2018). (PMID: 10.1021/acs.chemrev.8b0019929957926)
Materials Genome Initiative Strategic Plan. https://www.mgi.gov/sites/mgi/files/MGI-2021-Strategic-Plan.pdf .
Audus, D. J. & de Pablo, J. J. Polymer Informatics: Opportunities and Challenges. ACS Macro Lett. 6, 1078–1082 (2017). (PMID: 10.1021/acsmacrolett.7b00228292015355702941)
Eitouni, H. B. & Balsara, N. P. Thermodynamics of polymer blends. in Physical Properties of Polymers Handbook 339–356 (Springer, 2007).
Mark, J. E. Physical Properties of Polymers Handbook. vol. 1076 (Springer, 2007).
Tchoua, R. B. et al. Blending Education and Polymer Science: Semiautomated Creation of a Thermodynamic Property Database. J. Chem. Educ. 93, 1561–1568 (2016). (PMID: 10.1021/acs.jchemed.5b01032277955745082748)
Polymer Property Predictor and Database. https://pppdb.uchicago.edu/ .
Van Krevelen, D. W. & Te Nijenhuis, K. Chapter 8 - Interfacial Energy Properties. in Properties of Polymers (Fourth Edition) 229–244 https://doi.org/10.1016/B978-0-08-054819-7.00008-X (Elsevier, Amsterdam, 2009).
Grulke, E. A. Solubility Parameter Values. in Polymer Handbook 2336 pages (Brandrup, J., Immergut, E. H., Grulke, E. A. (Editor), 2003).
Small, P. A. Some factors affecting the solubility of polymers. J. Appl. Chem. 3, 71–80 (1953). (PMID: 10.1002/jctb.5010030205)
Hoy, K. L. New values of solubility parameters from vapor pressure data. Journal of Paint Technology 42, 76 (1970).
Matsen, M. W. & Schick, M. Stable and unstable phases of a diblock copolymer melt. Phys. Rev. Lett. 72, 2660–2663 (1994). (PMID: 10.1103/PhysRevLett.72.266010055940)
Bates, F. S. & Fredrickson, G. H. Block Copolymers—Designer Soft Materials. Phys. Today 52, 32–38 (1999). (PMID: 10.1063/1.882522)
Rebello, N. J. et al. The Block Copolymer Phase Behavior Database. J. Chem. Inf. Model. https://doi.org/10.1021/acs.jcim.4c00242 (2024).
MIT, O. L. & Rebello, N. J. olsenlabmit/BCDB: BCDB v1.0.5 Release. Zenodo https://doi.org/10.5281/zenodo.10739078 (2024).
Arora, A. et al. Random Forest Predictor for Diblock Copolymer Phase Behavior. ACS Macro Lett. 10, 1339–1345 (2021). (PMID: 10.1021/acsmacrolett.1c0052135549019)
Hutzschenreuter, D. et al. SmartCom Digital System of Units (D-SI) Guide for the use of the metadata-format used in metrology for the easy-to-use, safe, harmonised and unambiguous digital transfer of metrological data (2019).
Gillespie, A. Materials by Design. NIST https://www.nist.gov/feature-stories/materials-design (2019).
Ferrarese Lupi, F. et al. Tailored and Guided Dewetting of Block Copolymer/Homopolymer Blends. Macromolecules 53, 7207–7217 (2020). (PMID: 10.1021/acs.macromol.0c01126)
Ferrarese Lupi, F. et al. Fine Tuning of Lithographic Masks through Thin Films of PS-b-PMMA with Different Molar Mass by Rapid Thermal Processing. ACS Appl. Mater. Interfaces 6, 7180–7188 (2014). (PMID: 10.1021/am500307424738855)
Murataj, I. et al. Artificial fingerprints engraved through block-copolymers as nanoscale physical unclonable functions for authentication and identification. Nat. Commun. 15, 10576 (2024). (PMID: 10.1038/s41467-024-54492-83966336911634899)
Ferrarese Lupi, F. et al. Flash grafting of functional random copolymers for surface neutralization. J. Mater. Chem. C 2, 4909–4917 (2014). (PMID: 10.1039/C4TC00328D)
Magosso, C. et al. Enabling data-driven design of block copolymer self-assembly - Database. https://doi.org/10.5281/zenodo.13927939 .
Tseng, Y.-C. & Darling, S. B. Block Copolymer Nanostructures for Technology. Polymers 2, 470–489 (2010). (PMID: 10.3390/polym2040470)
Giammaria, T. J. et al. Micrometer-Scale Ordering of Silicon-Containing Block Copolymer Thin Films via High-Temperature Thermal Treatments. ACS Appl. Mater. Interfaces 8, 9897–9908 (2016). (PMID: 10.1021/acsami.6b0230027020526)
Aprile, G. et al. Toward Lateral Length Standards at the Nanoscale Based on Diblock Copolymers. ACS Appl. Mater. Interfaces 9, 15685–15697 (2017). (PMID: 10.1021/acsami.7b0050928397488)
ChiaraMagosso. ChiaraMagosso/BlockMetrology: BlockMetrology - code. Zenodo https://doi.org/10.5281/zenodo.15430176 (2025).
ChiaraMagosso. ChiaraMagosso/BlockMetrology. GitHub https://github.com/ChiaraMagosso/BlockMetrology .
ADAblock/ADAblock.ijm at ijMacro_updates_only · ChiaraMagosso/ADAblock. GitHub https://github.com/ChiaraMagosso/ADAblock/blob/ijMacro_updates_only/ADAblock.ijm .
Hong, L., Wan, Y. & Jain, A. Fingerprint image enhancement: algorithm and performance evaluation. IEEE Trans. Pattern Anal. Mach. Intell. 20, 777–789 (1998). (PMID: 10.1109/34.709565)
fingerprint-enhancer · PyPI. https://pypi.org/project/fingerprint-enhancer/ .
Murphy, J. N., Harris, K. D. & Buriak, J. M. Automated Defect and Correlation Length Analysis of Block Copolymer Thin Film Nanopatterns. PLOS ONE 10, e0133088 (2015). (PMID: 10.1371/journal.pone.0133088262079904514826)
Tu, K.-H. et al. Machine Learning Predictions of Block Copolymer Self-Assembly. Adv. Mater. 32, 2005713 (2020). (PMID: 10.1002/adma.202005713)
Ferrarese Lupi, F. et al. Rapid thermal processing of self-assembling block copolymer thin films. Nanotechnology 24, 315601 (2013). (PMID: 10.1088/0957-4484/24/31/31560123851718)
Ceresoli, M. et al. Evolution of lateral ordering in symmetric block copolymer thin films upon rapid thermal processing. Nanotechnology 25, 275601 (2014). (PMID: 10.1088/0957-4484/25/27/27560124960172)
Sparnacci, K. et al. Thermal Stability of Functional P(S-r-MMA) Random Copolymers for Nanolithographic Applications. ACS Appl. Mater. Interfaces 7, 3920–3930 (2015). (PMID: 10.1021/am509088s25664773)
Ceresoli, M. et al. Scaling of correlation length in lamellae forming PS-b-PMMA thin films upon high temperature rapid thermal treatments. J. Mater. Chem. C 3, 8618–8624 (2015). (PMID: 10.1039/C5TC01473E)
Ceresoli, M. et al. Neutral wetting brush layers for block copolymer thin films using homopolymer blends processed at high temperatures. Nanotechnology 26, 415603 (2015). (PMID: 10.1088/0957-4484/26/41/41560326404164)
Semenov, A. N. Contribution to the theory of microphase layering in block-copolymer melts. Zh Eksp Teor Fiz 88, 1242–1256 (1985).
Murataj, I. et al. Hybrid Metrology for Nanostructured Optical Metasurfaces. ACS Appl. Mater. Interfaces 15, 57992–58002 (2023). (PMID: 10.1021/acsami.3c139233799146010739581)
Yee, T. D., Watson, C. L., Roehling, J. D., Han, T. Y. & Hiszpanski, A. M. Fabrication and 3D tomographic characterization of nanowire arrays and meshes with tunable dimensions from shear-aligned block copolymers. Soft Matter 15, 4898–4904 (2019). (PMID: 10.1039/C9SM00303G31166358)
Lynd, N. A., Meuler, A. J. & Hillmyer, M. A. Polydispersity and block copolymer self-assembly. Prog. Polym. Sci. 33, 875–893 (2008). (PMID: 10.1016/j.progpolymsci.2008.07.003)
Doise, J. et al. Strategies for Increasing the Rate of Defect Annihilation in the Directed Self-Assembly of High-χ Block Copolymers. ACS Appl. Mater. Interfaces 11, 48419–48427 (2019). (PMID: 10.1021/acsami.9b1785831752485)
Wernecke, J. et al. Traceable GISAXS measurements for pitch determination of a 25 nm self-assembled polymer grating. J. Appl. Crystallogr. 47, 1912–1920 (2014). (PMID: 10.1107/S1600576714021050)
Versailles Project on Advanced Materials and Standards (VAMAS). http://www.vamas.org/ ; http://www.vamas.org/twa0/documents/2024_vamas_twa0_p3_DBC.pdf .
Wilkinson, M. D. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci. Data 3, 160018 (2016). (PMID: 10.1038/sdata.2016.18269782444792175)
Grootveld, M. & Gürdal, G. OpenAIRE Guide on how to find a trustworthy repository for your data. https://doi.org/10.5281/zenodo.4077212 (2018).
Grant Information:
21GRD01 European Association of National Metrology Institutes (EURAMET); 20FUN06-RMG2 European Association of National Metrology Institutes (EURAMET); 20FUN06 European Association of National Metrology Institutes (EURAMET)
Entry Date(s):
Date Created: 20250621 Latest Revision: 20250625
Update Code:
20250626
PubMed Central ID:
PMC12182569
DOI:
10.1038/s41597-025-05379-w
PMID:
40544169
Database:
MEDLINE
Weitere Informationen
Here we present a database composed of scanning electron microscope images of self-assembled block copolymers. The fabrication process parameters, structural properties and microscope information are all contained in the image metadata, making a group of images a database on its own. This approach has numerous advantages including ease of sharing, reusability of information and resilience against user errors. This database follows the digital International System of Units principles and is complemented by a graphical user interface for process metadata insertion and an automated algorithm for image analysis to retrieve structural properties of the nanostructures. Databases such as this one, together with data-driven approaches, enable users to rationally design new materials with the desired properties by understanding the relationship between fabrication parameters and material structure. The here reported database, that contains around 1747 images of lamellar phase and lying down cylinders self-assembled block copolymers along with associated metadata, is structured so it can be continuously expanded by the research community including also samples with different block copolymers morphologies.
(© 2025. The Author(s).)
Competing interests: The authors declare no competing financial interest. Certain equipment, instruments, software, or materials are identified in this paper in order to specify the experimental procedure adequately. Such identification is not intended to imply recommendation or endorsement of any product or service by NIST, nor is it intended to imply that the materials or equipment identified are necessarily the best available for the purpose.