دورية أكاديمية
On-Demand Optimization of Colorimetric Gas Sensors Using a Knowledge-Aware Algorithm-Driven Robotic Experimental Platform
| العنوان: | On-Demand Optimization of Colorimetric Gas Sensors Using a Knowledge-Aware Algorithm-Driven Robotic Experimental Platform |
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| المؤلفون: | Zhehong Ai, Longhan Zhang, Yangguan Chen, Yifan Long, Boyuan Li, Qingyu Dong, Yueming Wang, Jing Jiang |
| سنة النشر: | 2024 |
| مصطلحات موضوعية: | Biophysics, Medicine, Biotechnology, Science Policy, Space Science, Mathematical Sciences not elsewhere classified, wide dynamic range, satisfying multiple figures, customized multiobjective functions, best material globally, art detection limit, best ingredient composition, aware chemical descriptors, method could achieve, demand research objectives, demand materials synthesis, vast design space, demand optimization, search space, optimal composition, functional materials, aware algorithm, novel method, work demonstrates, traditional one, time methods, thereby achieving, study engineered, sensing array, research developed |
| الوصف: | Synthesizing the best material globally is challenging; it needs to know what and how much the best ingredient composition should be for satisfying multiple figures of merit simultaneously. Traditional one-variable-at-a-time methods are inefficient; the design-build-test-learn (DBTL) method could achieve the optimal composition from only a handful of ingredients. A vast design space needs to be explored to discover the possible global optimal composition for on-demand materials synthesis. This research developed a hypothesis-guided DBTL (H-DBTL) method combined with robots to expand the dimensions of the search space, thereby achieving a better global optimal performance. First, this study engineered the search space with knowledge-aware chemical descriptors and customized multiobjective functions to fulfill on-demand research objectives. To verify this concept, this novel method was used to optimize colorimetric ammonia sensors across a vast design space of as high as 19 variables, achieving two remarkable optimization goals within 1 week: first, a sensing array was developed for ammonia quantification of a wide dynamic range, from 0.5 to 500 ppm; second, a new state-of-the-art detection limit of 50 ppb was reached. This work demonstrates that the H-DBTL approach, combined with a robot, develops a novel paradigm for the on-demand optimization of functional materials. |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | unknown |
| العلاقة: | https://figshare.com/articles/journal_contribution/On-Demand_Optimization_of_Colorimetric_Gas_Sensors_Using_a_Knowledge-Aware_Algorithm-Driven_Robotic_Experimental_Platform/25194264Test |
| DOI: | 10.1021/acssensors.3c02043.s001 |
| الإتاحة: | https://doi.org/10.1021/acssensors.3c02043.s001Test https://figshare.com/articles/journal_contribution/On-Demand_Optimization_of_Colorimetric_Gas_Sensors_Using_a_Knowledge-Aware_Algorithm-Driven_Robotic_Experimental_Platform/25194264Test |
| حقوق: | CC BY-NC 4.0 |
| رقم الانضمام: | edsbas.C1601F32 |
| قاعدة البيانات: | BASE |
| DOI: | 10.1021/acssensors.3c02043.s001 |
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