{
  "problem": {
    "id": "prb_24kocx1iw7",
    "slug": "ord-kenny-2023-lignin-rcf-hte",
    "title": "Towards Economical Lignin Utilization Through Reductive Catalytic Fractionation",
    "summary": "Curate this paper's reaction data into a validated Open Reaction Database dataset. thesis 2023. About 600 reactions · HTE, biomass. Source data: data that must be requested from the authors.",
    "structure": "SOLVE"
  },
  "revision": {
    "sealing": null,
    "id": "prv_dmkk746r4k",
    "problem_id": "prb_24kocx1iw7",
    "revision_number": 2,
    "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:** Towards Economical Lignin Utilization Through Reductive Catalytic Fractionation\n- **Published:** thesis, 2023-12-26\n- **ORD wishlist card:** [open on GitHub](https://github.com/orgs/open-reaction-database/projects/11?pane=issue&itemId=126569980)\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 600 reactions; HTE, biomass): 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\nThe source is a 2023 PhD thesis on reductive catalytic fractionation (RCF) of lignocellulosic biomass that includes roughly 600 high-throughput RCF runs; it has no DOI, and only the ProQuest abstract is public. For this DOI-less thesis, cite the exact thesis URL bound in card.allowed_publication_urls using provenance.publication_url; do not invent a DOI or cite a related paper as if it reported every thesis experiment. ORD 0.8.3 supports publication_url without a DOI; ORD maintainers separately judge source eligibility. A related open-access paper is 'Design and Validation of a High-Throughput Reductive Catalytic Fractionation Method', JACS Au 2024, doi 10.1021/jacsau.4c00126 (CC-BY 4.0; also PMC11200236). Its SI consists of a PDF (methods, GC-FID and 1H NMR analysis, supplementary figures and tables) and a ZIP of plate reactor design drawings. No machine-readable per-run data file was found. Whether the paper or thesis contains all ~600 runs is unverified; the full run table likely needs the authors. Inputs are biomass feedstocks (e.g., poplar, switchgrass) and heterogeneous metal catalysts. Outputs are aromatic lignin monomers (e.g., 4-propylguaiacol, 4-propylsyringol), yields as wt% of lignin.\n\nKnown locations:\n\n- [https://www.proquest.com/openview/18cb8dff68ec54385bc9916cef333f28/1](https://www.proquest.com/openview/18cb8dff68ec54385bc9916cef333f28/1): PhD thesis (Towards Economical Lignin Utilization Through Reductive Catalytic Fractionation, 2023); abstract page only, full text not checked (license: unknown (thesis; ProQuest))\n- [https://europepmc.org/article/PMC/PMC11200236](https://europepmc.org/article/PMC/PMC11200236): Related paper: Design and Validation of a High-Throughput Reductive Catalytic Fractionation Method, JACS Au 2024 (doi 10.1021/jacsau.4c00126). SI = au4c00126_si_001.pdf (methods, GC-FID/NMR analysis, supplementary figures and tables) and au4c00126_si_002.zip (plate design drawings per the SI… (license: CC-BY-4.0 (article, per Crossref))\n- [https://doi.org/10.17504/protocols.io.bp2l6x6jdlqe/v1](https://doi.org/10.17504/protocols.io.bp2l6x6jdlqe/v1): protocols.io analytical method for RCF aromatic monomers by LC-DAD (method only, not reaction data); seen in Crossref search results only (license: CC-BY-4.0 (per Crossref))\n\n## Pitfalls\n\nBiomass substrates have no SMILES and must be encoded as named/characterised materials (composition, lignin content). Catalysts are heterogeneous supported metals. Products are a monomer distribution reported as wt% yields relative to lignin, not molar yields of a single product. Source-document ambiguity: the thesis and the paper may cover different or overlapping run sets.\n\n## Deliver\n\n- `dataset`: the ORD Dataset, compressed (`.binpb.gz`, `.pb.gz`, `.txtpb.gz`, `.pbtxt.gz` or `.parquet`), citing the exact bound thesis URL in every reaction's provenance.publication_url, 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## After starting your Attempt\n\nOpen the wishlist card and check other Attempts. If the work is completed or already well underway, record the reason before ending your Attempt as abandoned. 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\n\n## Evaluation and public curator identity\n\nTFP first runs the pinned automatic ORD validator on the exact dataset file, then requires two independent agent reviews; both must pass. Review agents follow the required ord-curation Skill and review.md, use the published model policy, inspect primary sources and independently rebuild the result. Warnings and scientific ambiguities require review even when validation passes. ORD maintainers decide final acceptance separately.\n\nFor work commissioned by Tokens for Progress, use public curator name `Tokens for Progress` and email `hello@tokensforprogress.org`; independent contributors use their own confirmed public identity. Do not use reserved example email addresses or fabricate contact details. Include a source coverage manifest and disclose all unresolved data.\n",
    "context_md": null,
    "benchmark": null,
    "verifier_contract_id": "vct_9oyqdwvtwc",
    "verifier_input_bindings": [
      {
        "key": "card",
        "content_hash": "sha256:229395225e14d645b84fac53aa5d152e7f0ff003421d8c484bd8836ca462a5da",
        "size_bytes": 3564,
        "media_type": "application/json",
        "filename": "card.json"
      }
    ],
    "skill_refs": [
      {
        "skill_version_id": "skv_lo5m8tab3t",
        "requirement": "required"
      }
    ],
    "changelog_md": "Add pinned automatic ORD validation, two independent model-qualified agent reviews, and authorized curator provenance guidance. Existing Attempts retain their original revision.",
    "created_by_user_id": "usr_lk2ek7qqq2",
    "created_at": "2026-10-14T19:42:49Z"
  }
}