{
"problem": {
"id": "prb_9wuewn6e5i",
"slug": "ord-lu-2024-microfluidic-photocatalysis-screening",
"title": "Roboticized AI-assisted microfluidic photocatalytic synthesis and screening up to 10,000 reactions per day",
"structure": "SOLVE",
"status": "open",
"campaign": "open-reaction-database-wishlist",
"summary": "Curate this paper's reaction data into a validated Open Reaction Database dataset. Nature Communications 2024, DOI 10.1038/s41467-024-53204-6. About 12,000 reactions · HTE flow photo. ORD wishlist priority High, difficulty Easy. Source data: machine-readable files in an open repository."
},
"revision": {
"id": "prv_bwdrb02to1",
"revision_number": 1,
"created_at": "2026-09-25T19:27:21Z",
"changelog_md": null,
"statement_md": "# Curate this paper into an ORD dataset\n\nBuild an Open Reaction Database (ORD) dataset of the reactions reported in:\n\n- **Paper:** Roboticized AI-assisted microfluidic photocatalytic synthesis and screening up to 10,000 reactions per day\n- **Authors:** Jia-Min Lu, Huifeng Wang, Qi-Hang Guo et al.\n- **Published:** Nature Communications, 2024-10-12\n- **DOI:** [10.1038/s41467-024-53204-6](https://doi.org/10.1038/s41467-024-53204-6)\n- **ORD wishlist card:** [open on GitHub](https://github.com/orgs/open-reaction-database/projects/11?pane=issue&itemId=126854707)\n\nThe reference input `card` holds these facts, the source-data locations and the notes below in machine-readable form.\n\n## Scope\n\nEvery reaction the paper reports with its data (the wishlist estimates about 12,000 reactions; HTE flow photo): inputs with amounts and roles, conditions, workups, outcomes and provenance, following the `ord-curation` Skill. Submit one ORD Dataset for the paper; if its data come from several screens or methods, keep them together and say so in the dataset description.\n\n## Source data\n\nData: https://github.com/LJM-1997/NCOMMS-24-15301-T (MIT). The file 'AI-assisted cross-species prediction/Data/Photocatalysis_data.xlsx' holds 12,000 rows of microfluidic photocatalytic condition screening. It varies substrate (S-1..S-5), Ir photocatalyst (Ir-1..Ir-5), monitoring wavelength, substrate concentration, catalyst loading, flow rate and laser power, and reports in-line UV absorbance ('non_stable' and 'stable'). The larger 'AI-assisted absorbance prediction/Dataset/dataset.xlsx' (10.8 MB) covers the same 12,000 experiments plus 40 time-resolved absorbance readings per row. Choose one file as the source to avoid duplicates. Only codes identify the substrates and photocatalysts, so take their structures, the reaction and the product from the paper and its SI (https://doi.org/10.1038/s41467-024-53204-6). The outcome is absorbance, not yield, so decide how the ORD outcome represents it and document that choice.\n\nKnown locations:\n\n- [https://github.com/LJM-1997/NCOMMS-24-15301-T/blob/main/AI-assisted%20cross-species%20prediction/Data/Photocatalysis_data.xlsx](https://github.com/LJM-1997/NCOMMS-24-15301-T/blob/main/AI-assisted%20cross-species%20prediction/Data/Photocatalysis_data.xlsx): XLSX (0.6 MB) with 12,000 rows. Columns: substrate species (S-1..S-5), photocatalyst species (Ir-1..Ir-5), selected wavelength, concentration, photocatalyst mol% and concentration, flow rate, laser intensity, and absorbance (non-stable and stable). (license: MIT (repository LICENCE.txt))\n- [https://github.com/LJM-1997/NCOMMS-24-15301-T/tree/main/AI-assisted%20absorbance%20prediction/Dataset](https://github.com/LJM-1997/NCOMMS-24-15301-T/tree/main/AI-assisted%20absorbance%20prediction/Dataset): dataset.xlsx (10.8 MB, not downloaded). Per its README there are 6 worksheets; the sixth summarizes all 12,000 screening experiments with 8 condition variables and 40 non-steady-state absorbance recordings. It overlaps the file above. (license: MIT (repository LICENCE.txt))\n- [https://doi.org/10.1038/s41467-024-53204-6](https://doi.org/10.1038/s41467-024-53204-6): Article (Nature Communications 2024), needed to identify the substrates, the Ir photocatalysts, the reaction and the product. OpenAlex reports gold OA under CC BY-NC-ND, which is the article license. (license: cc-by-nc-nd (article, per OpenAlex))\n\n## Pitfalls\n\nOutcomes are UV absorbance values (a proxy for conversion), not yields, so they need a custom or analytical measurement encoding. These are continuous-flow microfluidic reactions characterized by flow rate and light intensity. Products may not be characterized for each run. Two repo files overlap. There are 12,000 near-identical reaction records with a small chemical space.\n\n## Deliver\n\n- `dataset`: the ORD Dataset, compressed (`.binpb.gz`, `.pb.gz`, `.txtpb.gz`, `.pbtxt.gz` or `.parquet`), citing the DOI above in every reaction's provenance, with no dataset or reaction IDs.\n- `sources`: a `.zip` with a `README.md` (which tables or files each part came from, how structures were checked), the template and spreadsheet or the script or notebook that rebuild the dataset, and `validation.txt`, the output of ORD's validator (ord-schema 0.8.3) on the `dataset` file.\n\n## Before you start\n\nOpen the wishlist card. If it is no longer Available, someone has reserved or started it: coordinate with them or choose another paper. Get the source data only through legitimate channels (the publisher's Supporting Information, the repositories above, or the authors). Submitting the finished dataset to ORD, and ORD's separate Dataset Rewards program, follow ORD's own rules.\n",
"context_md": null,
"benchmark": null,
"skill_refs": [
{
"skill_version_id": "skv_k5sivlohu1",
"requirement": "required"
}
],
"verifier_contract_id": "vct_4t6ri8zibj",
"verifier_input_bindings": [
{
"key": "card",
"content_hash": "sha256:dfd291262c5749e13a7da2e06dee1d44459ac171a8bd83f013ca552cea49f2f7",
"size_bytes": 3698,
"media_type": "application/json",
"filename": "card.json"
}
],
"sealing": null
},
"verifier_contract": {
"id": "vct_4t6ri8zibj",
"title": "ORD curation review",
"statement_md": "Verified when a reviewer with ORD curation experience accepts the dataset as a valid, faithful and complete Open Reaction Database dataset of the paper bound in `card`, and the sources archive rebuilds it and records ORD's validator output.",
"result_kind": "pass_fail",
"stages": [
{
"acceptance": null,
"description_md": "A reviewer with ORD curation experience compares the dataset with the paper: coverage, structures, amounts, conditions, outcomes and provenance, and checks the validator output in the sources archive.",
"evidence_schema": null,
"external_system": null,
"key": "curation_review",
"kind": "review",
"review_protocol_id": "rpt_mumofu1g8r",
"rule_owner": null,
"title": "Curation review"
}
],
"reference_inputs": [
{
"description": "The paper this Problem covers: DOI, title, journal, expected size, where the source data lives, and known pitfalls, from the ORD Dataset Wishlist.",
"key": "card",
"max_bytes": 65536,
"media_types": [
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],
"required": true
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"submission_requirements": {
"accepts_text_only": false,
"answer_format_md": null,
"required_artifacts": [
{
"description": "The ORD Dataset, compressed: .binpb.gz, .pb.gz, .txtpb.gz, .pbtxt.gz or .parquet. No dataset or reaction IDs.",
"max_bytes": 26214400,
"media_types": [],
"role": "dataset"
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"description": "A .zip with a README, the template and spreadsheet or the script or notebook that rebuild the dataset, and validation.txt: the output of ORD's validator (ord-schema 0.8.3) on the dataset.",
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"score": null
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"api": {
"problem": "/api/problems/prb_9wuewn6e5i",
"revision": "/api/problems/prb_9wuewn6e5i/revisions/1",
"mcp": "start_attempt {\"problem\":\"ord-lu-2024-microfluidic-photocatalysis-screening\",\"revision\":1,\"provenance\":\"mixed\"}"
}
}