{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:C3NTMT3KUMRTBH33PRSJY4KXQS","short_pith_number":"pith:C3NTMT3K","schema_version":"1.0","canonical_sha256":"16db364f6aa323309f7b7c649c71578490329bb7bd5192994769f658efce42fa","source":{"kind":"arxiv","id":"2404.00167","version":1},"attestation_state":"computed","paper":{"title":"Electrical double layer and capacitance of TiO2 electrolyte interfaces from first principles simulations","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"physics.chem-ph","authors_text":"Abhinav S. Raman, Annabella Selloni, Chunyi Zhang, Marcos Calegari Andrade, Pablo Piaggi, Roberto Car, Xifan Wu, Yifan Li, Zachary K. Goldsmith","submitted_at":"2024-03-29T21:50:57Z","abstract_excerpt":"The electrical double layer (EDL) at aqueous solution-metal oxide interfaces critically affects many fundamental processes in electrochemistry, geology and biology, yet understanding its microscopic structure is challenging for both theory and experiments. Here we employ ab initio-based machine learning potentials including long-range electrostatics in large-scale atomistic simulations of the EDL at the TiO2-electrolyte interface. Our simulations provide a molecular-scale picture of the EDL that demonstrates the limitations of standard mean-field models. We further develop a method to accurate"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2404.00167","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"physics.chem-ph","submitted_at":"2024-03-29T21:50:57Z","cross_cats_sorted":[],"title_canon_sha256":"6256c283ada73de446a3dc33f6db1e78b135927cad845a9af680d82b33342c52","abstract_canon_sha256":"d4a6b98882e3f7055bd6b84386a00010eb900787fd89290385b095b40e97336c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:02:29.087124Z","signature_b64":"ZdJHHENaqW782tA9Sor0G0+lmXN/7hGrD/KiRgiZg510Ikvq5eUXbLnt7WwthtJ00oKuw/x4qO53AJ6guacdCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"16db364f6aa323309f7b7c649c71578490329bb7bd5192994769f658efce42fa","last_reissued_at":"2026-07-05T08:02:29.086644Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:02:29.086644Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Electrical double layer and capacitance of TiO2 electrolyte interfaces from first principles simulations","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"physics.chem-ph","authors_text":"Abhinav S. Raman, Annabella Selloni, Chunyi Zhang, Marcos Calegari Andrade, Pablo Piaggi, Roberto Car, Xifan Wu, Yifan Li, Zachary K. Goldsmith","submitted_at":"2024-03-29T21:50:57Z","abstract_excerpt":"The electrical double layer (EDL) at aqueous solution-metal oxide interfaces critically affects many fundamental processes in electrochemistry, geology and biology, yet understanding its microscopic structure is challenging for both theory and experiments. Here we employ ab initio-based machine learning potentials including long-range electrostatics in large-scale atomistic simulations of the EDL at the TiO2-electrolyte interface. Our simulations provide a molecular-scale picture of the EDL that demonstrates the limitations of standard mean-field models. We further develop a method to accurate"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.00167","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2404.00167/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2404.00167","created_at":"2026-07-05T08:02:29.086702+00:00"},{"alias_kind":"arxiv_version","alias_value":"2404.00167v1","created_at":"2026-07-05T08:02:29.086702+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.00167","created_at":"2026-07-05T08:02:29.086702+00:00"},{"alias_kind":"pith_short_12","alias_value":"C3NTMT3KUMRT","created_at":"2026-07-05T08:02:29.086702+00:00"},{"alias_kind":"pith_short_16","alias_value":"C3NTMT3KUMRTBH33","created_at":"2026-07-05T08:02:29.086702+00:00"},{"alias_kind":"pith_short_8","alias_value":"C3NTMT3K","created_at":"2026-07-05T08:02:29.086702+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.02948","citing_title":"CrossWeaver: Cross-modal Weaving for Arbitrary-Modality Semantic Segmentation","ref_index":43,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/C3NTMT3KUMRTBH33PRSJY4KXQS","json":"https://pith.science/pith/C3NTMT3KUMRTBH33PRSJY4KXQS.json","graph_json":"https://pith.science/api/pith-number/C3NTMT3KUMRTBH33PRSJY4KXQS/graph.json","events_json":"https://pith.science/api/pith-number/C3NTMT3KUMRTBH33PRSJY4KXQS/events.json","paper":"https://pith.science/paper/C3NTMT3K"},"agent_actions":{"view_html":"https://pith.science/pith/C3NTMT3KUMRTBH33PRSJY4KXQS","download_json":"https://pith.science/pith/C3NTMT3KUMRTBH33PRSJY4KXQS.json","view_paper":"https://pith.science/paper/C3NTMT3K","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2404.00167&json=true","fetch_graph":"https://pith.science/api/pith-number/C3NTMT3KUMRTBH33PRSJY4KXQS/graph.json","fetch_events":"https://pith.science/api/pith-number/C3NTMT3KUMRTBH33PRSJY4KXQS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/C3NTMT3KUMRTBH33PRSJY4KXQS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/C3NTMT3KUMRTBH33PRSJY4KXQS/action/storage_attestation","attest_author":"https://pith.science/pith/C3NTMT3KUMRTBH33PRSJY4KXQS/action/author_attestation","sign_citation":"https://pith.science/pith/C3NTMT3KUMRTBH33PRSJY4KXQS/action/citation_signature","submit_replication":"https://pith.science/pith/C3NTMT3KUMRTBH33PRSJY4KXQS/action/replication_record"}},"created_at":"2026-07-05T08:02:29.086702+00:00","updated_at":"2026-07-05T08:02:29.086702+00:00"}