STARLI | F131–F140 / 300 Flagship 13 Fission · Volume II · AI-Guided Micro/Nanosystems, Sampling and Validation Author: 33 | Nonclinical Research Design Working Edition v1.1 / Gate Annotation v1.2 | English Zenodo Ten-Volume Overview These ten STARLI flagship monographs form an AI, nanosystems and microfluidic research-design collection in the second volume of the Flagship 13 Fission atlas. The studies connect adaptive sampling, counterfactual inference, autonomous calibration, hybrid physical-learning models, failure prediction, interpretable quantum sensing, closed-loop latency, multi-agent coordination, external digital-twin validation and biological signal drift. Each volume defines a prospective measurement target and competing explanation. No result is claimed to be experimentally demonstrated. The collection contains ten separately authored 777-page English working editions: 300 prospective mathematical plates, 820 chapter openers, 4,100 separately paginated study positions, 2,000 Research Gates with six annotation fields per Gate, and 330 Phoenix Answer Embryos. These counts describe the publication structure, not validated scientific novelty. A companion publication ZIP includes the ten original PDFs and ten per-book Zenodo introductions. The Ten Research Directions F131 AI-Driven Microfluidic Sampling Strategy Optimization At The Limit — Examines recoverable analyte as a fraction of input under fixed sampling resources. Expert tuning with equal trials is the comparator; hidden human intervention is the rival. Blind recovery audits and out-of-distribution refusal test whether AI planning adds genuine value. F132 Nano-Quantum System Counterfactual Inference At The Limit — Tests held-out, intervention-specific counterfactual prediction error while retaining constraint-violation safeguards. Frozen open-loop policies anchor comparison; training-test leakage and nonreplayable observation-to-action chains can invalidate apparent autonomy. F133 AI-Driven Microrobotic Autonomous Calibration At The Limit — Uses blind-standard calibration bias after autonomous correction as a physical measurement target. Preregistered experimental design is the baseline. Extra experimental trials and any action outside the authority envelope defeat a claimed improvement. F134 Microscale Physical Model and Neural Network Coupling At The Limit — Challenges coupled learning and physical models against independently measured conservation residuals under withheld shifts. Matched expert tuning and hidden manual intervention are alternative explanations; unsafe confidence outside the tested domain is a stop condition. F135 AI-Driven Nanoscale Sampling Failure Prediction At The Limit — Proposes prospective fault warning lead time, in seconds, at a fixed false-alarm rate. Frozen open-loop policies and unselected failure cases distinguish early warning from hindsight leakage. Recovery outcomes and complete action replay remain necessary. F136 Quantum Sensor Interpretable AI At The Limit — Asks whether AI explanations agree with mechanism-specific physical ablations, rather than merely producing plausible narratives. The proposed witness is an agreement fraction, with counterfactual error and calibrated sensing as safeguards. More experimental trials are a competing account. F137 Microrobotic Closed-Loop Control Latency At The Limit — Measures the full sensor-to-physical-action delay, in seconds, inside a declared operating envelope. Expert-tuned controls under matched resources establish the comparison. Hidden human intervention and high-confidence out-of-domain action must be ruled out. F138 AI Multi-Agent Sampling Mission Coordination At The Limit — Evaluates calibration-adjusted joint sampling mission completion as a fraction under fairness and safety vetoes. Frozen open-loop coordination and identical observation budgets serve as controls; training-test leakage and missing action provenance are rival explanations. F139 Nano-Quantum Digital Twin External Validation At The Limit — Freezes a digital twin before evaluating out-of-site prediction error against independent measurements. Preregistered experimental design is the reference; additional experiments, leakage or operating outside the authorized action envelope cannot be counted as predictive success. F140 AI Biological Signal Concept Drift Identification At The Limit — Measures detection delay, in seconds, at a fixed false-alarm rate, separating biological-state drift from sensor and calibration changes. Equal-trial expert tuning and hidden manual correction are comparators. Confident action on unsupported states triggers a hard stop. Scope, Falsifiability and Evidence Boundaries All volumes are prospective nonclinical research designs for traceable samples, authorized ex-vivo, simulated or closed-bench settings; no laboratory results, clinical efficacy or quantum advantage are reported. Principal comparisons include equal-budget expert tuning, frozen open-loop control and preregistered experimental design. Preserved sample identity, independent measurement, held-out blind challenges, negative-run reporting and hard safety stops are required. The source QA marks structural checks as passed, but physical derivation review, causal distinctness, claim-level DOI matching and exhaustive visual inspection as partial or unfinished. Selected Established Methodological References Degen, Reinhard & Cappellaro (2017) · Quantum sensing · DOI 10.1103/RevModPhys.89.035002 | Koscher et al. (2023) · Autonomous molecular discovery · DOI 10.1126/science.adi1407 | Seo, Paik & Yim (2019) · Modular reconfigurable robotics · DOI 10.1146/annurev-control-053018-023834 These references support established methods, not the untested STARLI-specific hypotheses. Individual flagship source-to-claim DOI mappings remain to be independently verified. Join the STARLI Research Community Explore more than 100 interdisciplinary research themes and follow the evolving STARLI Research Gate community: https://zenodo.org/communities/starli-research-gate/ We do not need to know which door future generations will choose to open. We only hope that when they open it, they will find a little light to guide their way.

BibTeX

@article{allodium:10.5281/zenodo.23915126,
  title = {STARLI | F131–F140 / 300 Flagship 13 Fission · Volume II · AI-Guided Micro/Nanosystems, Sampling and Validation Author: 33  |  Nonclinical Research Design Working Edition v1.1 / Gate Annotation v1.2  |  English Zenodo Ten-Volume Overview These ten STARLI flagship monographs form an AI, nanosystems and microfluidic research-design collection in the second volume of the Flagship 13 Fission atlas. The studies connect adaptive sampling, counterfactual inference, autonomous calibration, hybrid physical-learning models, failure prediction, interpretable quantum sensing, closed-loop latency, multi-agent coordination, external digital-twin validation and biological signal drift. Each volume defines a prospective measurement target and competing explanation. No result is claimed to be experimentally demonstrated. The collection contains ten separately authored 777-page English working editions: 300 prospective mathematical plates, 820 chapter openers, 4,100 separately paginated study positions, 2,000 Research Gates with six annotation fields per Gate, and 330 Phoenix Answer Embryos. These counts describe the publication structure, not validated scientific novelty. A companion publication ZIP includes the ten original PDFs and ten per-book Zenodo introductions. The Ten Research Directions F131  AI-Driven Microfluidic Sampling Strategy Optimization At The Limit — Examines recoverable analyte as a fraction of input under fixed sampling resources. Expert tuning with equal trials is the comparator; hidden human intervention is the rival. Blind recovery audits and out-of-distribution refusal test whether AI planning adds genuine value. F132  Nano-Quantum System Counterfactual Inference At The Limit — Tests held-out, intervention-specific counterfactual prediction error while retaining constraint-violation safeguards. Frozen open-loop policies anchor comparison; training-test leakage and nonreplayable observation-to-action chains can invalidate apparent autonomy. F133  AI-Driven Microrobotic Autonomous Calibration At The Limit — Uses blind-standard calibration bias after autonomous correction as a physical measurement target. Preregistered experimental design is the baseline. Extra experimental trials and any action outside the authority envelope defeat a claimed improvement. F134  Microscale Physical Model and Neural Network Coupling At The Limit — Challenges coupled learning and physical models against independently measured conservation residuals under withheld shifts. Matched expert tuning and hidden manual intervention are alternative explanations; unsafe confidence outside the tested domain is a stop condition. F135  AI-Driven Nanoscale Sampling Failure Prediction At The Limit — Proposes prospective fault warning lead time, in seconds, at a fixed false-alarm rate. Frozen open-loop policies and unselected failure cases distinguish early warning from hindsight leakage. Recovery outcomes and complete action replay remain necessary. F136  Quantum Sensor Interpretable AI At The Limit — Asks whether AI explanations agree with mechanism-specific physical ablations, rather than merely producing plausible narratives. The proposed witness is an agreement fraction, with counterfactual error and calibrated sensing as safeguards. More experimental trials are a competing account. F137  Microrobotic Closed-Loop Control Latency At The Limit — Measures the full sensor-to-physical-action delay, in seconds, inside a declared operating envelope. Expert-tuned controls under matched resources establish the comparison. Hidden human intervention and high-confidence out-of-domain action must be ruled out. F138  AI Multi-Agent Sampling Mission Coordination At The Limit — Evaluates calibration-adjusted joint sampling mission completion as a fraction under fairness and safety vetoes. Frozen open-loop coordination and identical observation budgets serve as controls; training-test leakage and missing action provenance are rival explanations. F139  Nano-Quantum Digital Twin External Validation At The Limit — Freezes a digital twin before evaluating out-of-site prediction error against independent measurements. Preregistered experimental design is the reference; additional experiments, leakage or operating outside the authorized action envelope cannot be counted as predictive success. F140  AI Biological Signal Concept Drift Identification At The Limit — Measures detection delay, in seconds, at a fixed false-alarm rate, separating biological-state drift from sensor and calibration changes. Equal-trial expert tuning and hidden manual correction are comparators. Confident action on unsupported states triggers a hard stop. Scope, Falsifiability and Evidence Boundaries All volumes are prospective nonclinical research designs for traceable samples, authorized ex-vivo, simulated or closed-bench settings; no laboratory results, clinical efficacy or quantum advantage are reported. Principal comparisons include equal-budget expert tuning, frozen open-loop control and preregistered experimental design. Preserved sample identity, independent measurement, held-out blind challenges, negative-run reporting and hard safety stops are required. The source QA marks structural checks as passed, but physical derivation review, causal distinctness, claim-level DOI matching and exhaustive visual inspection as partial or unfinished. Selected Established Methodological References Degen, Reinhard \& Cappellaro (2017) · Quantum sensing · DOI 10.1103/RevModPhys.89.035002   |   Koscher et al. (2023) · Autonomous molecular discovery · DOI 10.1126/science.adi1407   |   Seo, Paik \& Yim (2019) · Modular reconfigurable robotics · DOI 10.1146/annurev-control-053018-023834 These references support established methods, not the untested STARLI-specific hypotheses. Individual flagship source-to-claim DOI mappings remain to be independently verified. Join the STARLI Research Community Explore more than 100 interdisciplinary research themes and follow the evolving STARLI Research Gate community: https://zenodo.org/communities/starli-research-gate/ We do not need to know which door future generations will choose to open. We only hope that when they open it, they will find a little light to guide their way.},
  author = {33},
  year = {2026},
  doi = {10.5281/zenodo.23915126},
  url = {https://doi.org/10.5281/zenodo.23915126}
}

RIS

TY  - JOUR
TI  - STARLI | F131–F140 / 300 Flagship 13 Fission · Volume II · AI-Guided Micro/Nanosystems, Sampling and Validation Author: 33  |  Nonclinical Research Design Working Edition v1.1 / Gate Annotation v1.2  |  English Zenodo Ten-Volume Overview These ten STARLI flagship monographs form an AI, nanosystems and microfluidic research-design collection in the second volume of the Flagship 13 Fission atlas. The studies connect adaptive sampling, counterfactual inference, autonomous calibration, hybrid physical-learning models, failure prediction, interpretable quantum sensing, closed-loop latency, multi-agent coordination, external digital-twin validation and biological signal drift. Each volume defines a prospective measurement target and competing explanation. No result is claimed to be experimentally demonstrated. The collection contains ten separately authored 777-page English working editions: 300 prospective mathematical plates, 820 chapter openers, 4,100 separately paginated study positions, 2,000 Research Gates with six annotation fields per Gate, and 330 Phoenix Answer Embryos. These counts describe the publication structure, not validated scientific novelty. A companion publication ZIP includes the ten original PDFs and ten per-book Zenodo introductions. The Ten Research Directions F131  AI-Driven Microfluidic Sampling Strategy Optimization At The Limit — Examines recoverable analyte as a fraction of input under fixed sampling resources. Expert tuning with equal trials is the comparator; hidden human intervention is the rival. Blind recovery audits and out-of-distribution refusal test whether AI planning adds genuine value. F132  Nano-Quantum System Counterfactual Inference At The Limit — Tests held-out, intervention-specific counterfactual prediction error while retaining constraint-violation safeguards. Frozen open-loop policies anchor comparison; training-test leakage and nonreplayable observation-to-action chains can invalidate apparent autonomy. F133  AI-Driven Microrobotic Autonomous Calibration At The Limit — Uses blind-standard calibration bias after autonomous correction as a physical measurement target. Preregistered experimental design is the baseline. Extra experimental trials and any action outside the authority envelope defeat a claimed improvement. F134  Microscale Physical Model and Neural Network Coupling At The Limit — Challenges coupled learning and physical models against independently measured conservation residuals under withheld shifts. Matched expert tuning and hidden manual intervention are alternative explanations; unsafe confidence outside the tested domain is a stop condition. F135  AI-Driven Nanoscale Sampling Failure Prediction At The Limit — Proposes prospective fault warning lead time, in seconds, at a fixed false-alarm rate. Frozen open-loop policies and unselected failure cases distinguish early warning from hindsight leakage. Recovery outcomes and complete action replay remain necessary. F136  Quantum Sensor Interpretable AI At The Limit — Asks whether AI explanations agree with mechanism-specific physical ablations, rather than merely producing plausible narratives. The proposed witness is an agreement fraction, with counterfactual error and calibrated sensing as safeguards. More experimental trials are a competing account. F137  Microrobotic Closed-Loop Control Latency At The Limit — Measures the full sensor-to-physical-action delay, in seconds, inside a declared operating envelope. Expert-tuned controls under matched resources establish the comparison. Hidden human intervention and high-confidence out-of-domain action must be ruled out. F138  AI Multi-Agent Sampling Mission Coordination At The Limit — Evaluates calibration-adjusted joint sampling mission completion as a fraction under fairness and safety vetoes. Frozen open-loop coordination and identical observation budgets serve as controls; training-test leakage and missing action provenance are rival explanations. F139  Nano-Quantum Digital Twin External Validation At The Limit — Freezes a digital twin before evaluating out-of-site prediction error against independent measurements. Preregistered experimental design is the reference; additional experiments, leakage or operating outside the authorized action envelope cannot be counted as predictive success. F140  AI Biological Signal Concept Drift Identification At The Limit — Measures detection delay, in seconds, at a fixed false-alarm rate, separating biological-state drift from sensor and calibration changes. Equal-trial expert tuning and hidden manual correction are comparators. Confident action on unsupported states triggers a hard stop. Scope, Falsifiability and Evidence Boundaries All volumes are prospective nonclinical research designs for traceable samples, authorized ex-vivo, simulated or closed-bench settings; no laboratory results, clinical efficacy or quantum advantage are reported. Principal comparisons include equal-budget expert tuning, frozen open-loop control and preregistered experimental design. Preserved sample identity, independent measurement, held-out blind challenges, negative-run reporting and hard safety stops are required. The source QA marks structural checks as passed, but physical derivation review, causal distinctness, claim-level DOI matching and exhaustive visual inspection as partial or unfinished. Selected Established Methodological References Degen, Reinhard & Cappellaro (2017) · Quantum sensing · DOI 10.1103/RevModPhys.89.035002   |   Koscher et al. (2023) · Autonomous molecular discovery · DOI 10.1126/science.adi1407   |   Seo, Paik & Yim (2019) · Modular reconfigurable robotics · DOI 10.1146/annurev-control-053018-023834 These references support established methods, not the untested STARLI-specific hypotheses. Individual flagship source-to-claim DOI mappings remain to be independently verified. Join the STARLI Research Community Explore more than 100 interdisciplinary research themes and follow the evolving STARLI Research Gate community: https://zenodo.org/communities/starli-research-gate/ We do not need to know which door future generations will choose to open. We only hope that when they open it, they will find a little light to guide their way.
AU  - 33
PY  - 2026
DO  - 10.5281/zenodo.23915126
UR  - https://doi.org/10.5281/zenodo.23915126
ER  - 

APA

33 (2026). STARLI | F131–F140 / 300 Flagship 13 Fission · Volume II · AI-Guided Micro/Nanosystems, Sampling and Validation Author: 33 | Nonclinical Research Design Working Edition v1.1 / Gate Annotation v1.2 | English Zenodo Ten-Volume Overview These ten STARLI flagship monographs form an AI, nanosystems and microfluidic research-design collection in the second volume of the Flagship 13 Fission atlas. The studies connect adaptive sampling, counterfactual inference, autonomous calibration, hybrid physical-learning models, failure prediction, interpretable quantum sensing, closed-loop latency, multi-agent coordination, external digital-twin validation and biological signal drift. Each volume defines a prospective measurement target and competing explanation. No result is claimed to be experimentally demonstrated. The collection contains ten separately authored 777-page English working editions: 300 prospective mathematical plates, 820 chapter openers, 4,100 separately paginated study positions, 2,000 Research Gates with six annotation fields per Gate, and 330 Phoenix Answer Embryos. These counts describe the publication structure, not validated scientific novelty. A companion publication ZIP includes the ten original PDFs and ten per-book Zenodo introductions. The Ten Research Directions F131 AI-Driven Microfluidic Sampling Strategy Optimization At The Limit — Examines recoverable analyte as a fraction of input under fixed sampling resources. Expert tuning with equal trials is the comparator; hidden human intervention is the rival. Blind recovery audits and out-of-distribution refusal test whether AI planning adds genuine value. F132 Nano-Quantum System Counterfactual Inference At The Limit — Tests held-out, intervention-specific counterfactual prediction error while retaining constraint-violation safeguards. Frozen open-loop policies anchor comparison; training-test leakage and nonreplayable observation-to-action chains can invalidate apparent autonomy. F133 AI-Driven Microrobotic Autonomous Calibration At The Limit — Uses blind-standard calibration bias after autonomous correction as a physical measurement target. Preregistered experimental design is the baseline. Extra experimental trials and any action outside the authority envelope defeat a claimed improvement. F134 Microscale Physical Model and Neural Network Coupling At The Limit — Challenges coupled learning and physical models against independently measured conservation residuals under withheld shifts. Matched expert tuning and hidden manual intervention are alternative explanations; unsafe confidence outside the tested domain is a stop condition. F135 AI-Driven Nanoscale Sampling Failure Prediction At The Limit — Proposes prospective fault warning lead time, in seconds, at a fixed false-alarm rate. Frozen open-loop policies and unselected failure cases distinguish early warning from hindsight leakage. Recovery outcomes and complete action replay remain necessary. F136 Quantum Sensor Interpretable AI At The Limit — Asks whether AI explanations agree with mechanism-specific physical ablations, rather than merely producing plausible narratives. The proposed witness is an agreement fraction, with counterfactual error and calibrated sensing as safeguards. More experimental trials are a competing account. F137 Microrobotic Closed-Loop Control Latency At The Limit — Measures the full sensor-to-physical-action delay, in seconds, inside a declared operating envelope. Expert-tuned controls under matched resources establish the comparison. Hidden human intervention and high-confidence out-of-domain action must be ruled out. F138 AI Multi-Agent Sampling Mission Coordination At The Limit — Evaluates calibration-adjusted joint sampling mission completion as a fraction under fairness and safety vetoes. Frozen open-loop coordination and identical observation budgets serve as controls; training-test leakage and missing action provenance are rival explanations. F139 Nano-Quantum Digital Twin External Validation At The Limit — Freezes a digital twin before evaluating out-of-site prediction error against independent measurements. Preregistered experimental design is the reference; additional experiments, leakage or operating outside the authorized action envelope cannot be counted as predictive success. F140 AI Biological Signal Concept Drift Identification At The Limit — Measures detection delay, in seconds, at a fixed false-alarm rate, separating biological-state drift from sensor and calibration changes. Equal-trial expert tuning and hidden manual correction are comparators. Confident action on unsupported states triggers a hard stop. Scope, Falsifiability and Evidence Boundaries All volumes are prospective nonclinical research designs for traceable samples, authorized ex-vivo, simulated or closed-bench settings; no laboratory results, clinical efficacy or quantum advantage are reported. Principal comparisons include equal-budget expert tuning, frozen open-loop control and preregistered experimental design. Preserved sample identity, independent measurement, held-out blind challenges, negative-run reporting and hard safety stops are required. The source QA marks structural checks as passed, but physical derivation review, causal distinctness, claim-level DOI matching and exhaustive visual inspection as partial or unfinished. Selected Established Methodological References Degen, Reinhard & Cappellaro (2017) · Quantum sensing · DOI 10.1103/RevModPhys.89.035002 | Koscher et al. (2023) · Autonomous molecular discovery · DOI 10.1126/science.adi1407 | Seo, Paik & Yim (2019) · Modular reconfigurable robotics · DOI 10.1146/annurev-control-053018-023834 These references support established methods, not the untested STARLI-specific hypotheses. Individual flagship source-to-claim DOI mappings remain to be independently verified. Join the STARLI Research Community Explore more than 100 interdisciplinary research themes and follow the evolving STARLI Research Gate community: https://zenodo.org/communities/starli-research-gate/ We do not need to know which door future generations will choose to open. We only hope that when they open it, they will find a little light to guide their way.. https://doi.org/10.5281/zenodo.23915126