{"id":"W4412656405","doi":"10.1021/acsami.5c06970","title":"A Scalable Synthetic Approach for Producing Homogeneous, Large Area 2D Highly Conductive Polymers","year":2025,"lang":"en","type":"article","venue":"ACS Applied Materials & Interfaces","topic":"Conducting polymers and applications","field":"Materials Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Discovery Centre","funders":"Australian Research Council; La Trobe University; Commonwealth Scientific and Industrial Research Organisation; Australian Government","keywords":"Materials science; Homogeneous; Polymer; Electrical conductor; Nanotechnology; Conductive polymer; Scalability; Composite material; Computer science; Statistical physics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007073745,0.0003750881,0.0005663692,0.0001496022,0.0005137235,0.0003663656,0.0006130041,0.0001514544,0.0003850555],"category_scores_gemma":[0.00007331822,0.0003351246,0.00003614919,0.0002548927,0.0002111159,0.0001449882,0.0002995038,0.00009267564,0.00007845827],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008086435,"about_ca_system_score_gemma":0.0001159284,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001707729,"about_ca_topic_score_gemma":0.000007833798,"domain_scores_codex":[0.9974547,0.00005517716,0.0005814037,0.001026787,0.0001841133,0.0006978036],"domain_scores_gemma":[0.9986843,0.0001317851,0.0002754594,0.0007334637,0.0001061061,0.00006886237],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001335869,0.0001783174,0.000003373456,0.0001864204,0.00005196174,3.365712e-7,0.0004616169,0.00009240285,0.9906399,0.005897882,0.001816182,0.000538079],"study_design_scores_gemma":[0.0005096559,0.00003950099,0.000008296494,0.00006186864,0.0001190445,0.000005674663,0.001106304,0.000005818116,0.9961379,0.0006905005,0.0009674635,0.0003479833],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9901984,0.000420803,0.002115563,0.0004568766,0.0007101504,0.001567253,0.0004969873,0.0002722801,0.00376171],"genre_scores_gemma":[0.9903575,0.00001356085,0.00390658,0.0002340092,0.0001220558,0.0017349,0.0000797097,0.0000509231,0.003500761],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005498048,"threshold_uncertainty_score":0.9999101,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01913354221461675,"score_gpt":0.26056820656129,"score_spread":0.2414346643466732,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}