{"id":"W4391357327","doi":"10.1039/d3mh01884a","title":"Kirigami-enabled stretchable laser-induced graphene heaters for wearable thermotherapy","year":2024,"lang":"en","type":"article","venue":"Materials Horizons","topic":"Advanced Sensor and Energy Harvesting Materials","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; China Scholarship Council; University of Toronto; Canada Foundation for Innovation","keywords":"Graphene; Materials science; Wearable computer; Laser; Nanotechnology; Inkwell; Optoelectronics; Composite material; Optics; Computer science; Embedded system","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002618771,0.0003390896,0.0004205657,0.0001404582,0.0001312081,0.000391756,0.0001791818,0.0001502167,0.000623402],"category_scores_gemma":[0.0000192257,0.0003062168,0.00009810175,0.0001971596,0.00002988569,0.0002711534,0.00001915372,0.00007127483,0.0001093193],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000656796,"about_ca_system_score_gemma":0.00002322454,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007629766,"about_ca_topic_score_gemma":0.00001007333,"domain_scores_codex":[0.9984799,0.00004565993,0.0003851299,0.0003615922,0.0001235724,0.0006041686],"domain_scores_gemma":[0.9994098,0.00009402807,0.00002727208,0.0003252691,0.00003305713,0.0001105879],"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":[0.00004329336,0.00001213718,0.000001339957,0.0003064237,0.0001028101,0.00001238059,0.0001050434,0.00589648,0.9884562,0.0006409765,0.002156226,0.002266687],"study_design_scores_gemma":[0.000316315,0.0001444624,0.00001198719,0.0002001156,0.00003527057,0.000009455478,0.00004209107,0.001577567,0.9743688,0.002771343,0.02012218,0.0004003849],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9886475,0.000262949,0.002397394,0.0001037156,0.004665583,0.0003937629,0.000333416,0.001906321,0.001289421],"genre_scores_gemma":[0.9959457,0.0002403931,0.001644651,0.00004197329,0.0006567555,0.0002443557,0.00008938707,0.0002022007,0.0009346122],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01796595,"threshold_uncertainty_score":0.999939,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01602620279529624,"score_gpt":0.2382193064695727,"score_spread":0.2221931036742764,"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."}}