{"id":"W4413744059","doi":"10.1039/d5nr01453k","title":"Predictively designing a linear solid–liquid triboelectric nanogenerator for underwater tactile sensing","year":2025,"lang":"en","type":"article","venue":"Nanoscale","topic":"Advanced Sensor and Energy Harvesting Materials","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Triboelectric effect; Nanogenerator; Underwater; Materials science; Acoustics; Tactile sensor; Solid surface; Nanotechnology; Computer science; Composite material; Chemistry; Artificial intelligence; Physics; Robot; Geology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002341651,0.0002314826,0.0002145397,0.0001159632,0.0001495146,0.0002870508,0.0006384301,0.0003571795,0.0008420757],"category_scores_gemma":[0.0002948705,0.0001771122,0.0001333137,0.00011193,0.0003455247,0.0005758132,0.000361807,0.0003911461,0.0002964346],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002023938,"about_ca_system_score_gemma":0.0002843688,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001233743,"about_ca_topic_score_gemma":0.0003246224,"domain_scores_codex":[0.999918,0.000009255205,0.000005138706,0.00002205369,0.00003592385,0.000009703757],"domain_scores_gemma":[0.9999046,0.00003368197,0.00002635054,0.00001151509,0.00001552787,0.00000837907],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006544411,0.00008170651,0.0003785074,0.000219684,0.0000133726,0.0001634787,0.00006711501,0.03222252,0.9305389,0.005951223,0.0002567304,0.03004127],"study_design_scores_gemma":[0.00003014451,0.0004902295,0.0002976047,0.00001537213,0.00001358485,0.0001434504,0.00003303136,0.4568766,0.5357701,0.001743723,0.004560434,0.00002575047],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2350034,0.0007668947,0.7570922,0.0005380008,0.0001213053,0.000242342,0.00008955623,0.0005926611,0.005553614],"genre_scores_gemma":[0.8466687,0.0002800845,0.1509292,0.0001027402,0.00001726822,0.0001119956,0.00003459626,0.00003001184,0.001825432],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0008420757,"threshold_uncertainty_score":0.002816975,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01400044379873261,"score_gpt":0.2511187333787906,"score_spread":0.237118289580058,"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."}}