{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:ZX3ESVDAFMOF7FF6TQCPOXAVKB","short_pith_number":"pith:ZX3ESVDA","schema_version":"1.0","canonical_sha256":"cdf64954602b1c5f94be9c04f75c15504666528d78683399e9bfcdd9a3945fea","source":{"kind":"arxiv","id":"2101.11402","version":1},"attestation_state":"computed","paper":{"title":"Identification of particle mixtures using machine-learning-assisted laser diffraction analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["physics.optics"],"primary_cat":"eess.IV","authors_text":"Alfred U'Ren, Arturo Villegas, Juan P. Torres, Mario A. Quiroz-Juarez, Roberto de J. Leon-Montiel","submitted_at":"2021-01-25T15:42:56Z","abstract_excerpt":"We demonstrate a smart laser-diffraction analysis technique for particle mixture identification. We retrieve information about the size, geometry, and ratio concentration of two-component heterogeneous particle mixtures with an efficiency above 92%. In contrast to commonly-used laser diffraction schemes -- in which a large number of detectors is needed -- our machine-learning-assisted protocol makes use of a single far-field diffraction pattern, contained within a small angle ($\\sim 0.26^{\\circ}$) around the light propagation axis. Because of its reliability and ease of implementation, our wor"},"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":"2101.11402","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2021-01-25T15:42:56Z","cross_cats_sorted":["physics.optics"],"title_canon_sha256":"c515970c83f998fa1b038c7a4a5eb07b59fa2f945fbeb29f1009e42b28d51f13","abstract_canon_sha256":"c8147f57e7a008ae1c3a7c7dc77539205f96a244170c8a9b40f49b17ecf20af3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:53:34.662978Z","signature_b64":"GOz/kuwFUA66thxRfMt2gFHtFn2oXuSvFjma5akSfbdSsvah4S9kiVwNUEWqiSz/fJUSdHHuJDq+hgT8OeKyDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cdf64954602b1c5f94be9c04f75c15504666528d78683399e9bfcdd9a3945fea","last_reissued_at":"2026-07-05T03:53:34.662497Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:53:34.662497Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Identification of particle mixtures using machine-learning-assisted laser diffraction analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["physics.optics"],"primary_cat":"eess.IV","authors_text":"Alfred U'Ren, Arturo Villegas, Juan P. Torres, Mario A. Quiroz-Juarez, Roberto de J. Leon-Montiel","submitted_at":"2021-01-25T15:42:56Z","abstract_excerpt":"We demonstrate a smart laser-diffraction analysis technique for particle mixture identification. We retrieve information about the size, geometry, and ratio concentration of two-component heterogeneous particle mixtures with an efficiency above 92%. In contrast to commonly-used laser diffraction schemes -- in which a large number of detectors is needed -- our machine-learning-assisted protocol makes use of a single far-field diffraction pattern, contained within a small angle ($\\sim 0.26^{\\circ}$) around the light propagation axis. Because of its reliability and ease of implementation, our wor"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2101.11402","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/2101.11402/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":"2101.11402","created_at":"2026-07-05T03:53:34.662555+00:00"},{"alias_kind":"arxiv_version","alias_value":"2101.11402v1","created_at":"2026-07-05T03:53:34.662555+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2101.11402","created_at":"2026-07-05T03:53:34.662555+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZX3ESVDAFMOF","created_at":"2026-07-05T03:53:34.662555+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZX3ESVDAFMOF7FF6","created_at":"2026-07-05T03:53:34.662555+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZX3ESVDA","created_at":"2026-07-05T03:53:34.662555+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZX3ESVDAFMOF7FF6TQCPOXAVKB","json":"https://pith.science/pith/ZX3ESVDAFMOF7FF6TQCPOXAVKB.json","graph_json":"https://pith.science/api/pith-number/ZX3ESVDAFMOF7FF6TQCPOXAVKB/graph.json","events_json":"https://pith.science/api/pith-number/ZX3ESVDAFMOF7FF6TQCPOXAVKB/events.json","paper":"https://pith.science/paper/ZX3ESVDA"},"agent_actions":{"view_html":"https://pith.science/pith/ZX3ESVDAFMOF7FF6TQCPOXAVKB","download_json":"https://pith.science/pith/ZX3ESVDAFMOF7FF6TQCPOXAVKB.json","view_paper":"https://pith.science/paper/ZX3ESVDA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2101.11402&json=true","fetch_graph":"https://pith.science/api/pith-number/ZX3ESVDAFMOF7FF6TQCPOXAVKB/graph.json","fetch_events":"https://pith.science/api/pith-number/ZX3ESVDAFMOF7FF6TQCPOXAVKB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZX3ESVDAFMOF7FF6TQCPOXAVKB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZX3ESVDAFMOF7FF6TQCPOXAVKB/action/storage_attestation","attest_author":"https://pith.science/pith/ZX3ESVDAFMOF7FF6TQCPOXAVKB/action/author_attestation","sign_citation":"https://pith.science/pith/ZX3ESVDAFMOF7FF6TQCPOXAVKB/action/citation_signature","submit_replication":"https://pith.science/pith/ZX3ESVDAFMOF7FF6TQCPOXAVKB/action/replication_record"}},"created_at":"2026-07-05T03:53:34.662555+00:00","updated_at":"2026-07-05T03:53:34.662555+00:00"}