인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Abstract Plant pathogenic bacteria are one of the most important threats to agriculture production, diminishing the growth and development of host crops. Bacterial diagnostic is based on traditional microbiological methods, including isolation, purification, and a further confirmatory immunological or molecular test for accurate identification. In this work, we present the design and fabrication of a plasmonic optoelectronic sensor based on seven “olfactory receptors” formed by seven gold nanoparticle (AuNP) morphologies, including nano bones, nanospheres, nanorods, and nano shuttles with two sizes and nanostars. The AuNPs work as a central part of the sensor for color change analysis, the principle of which is based on the reduction of Tollens’ reagent with aldehydes produced by the tested phytopathogenic bacteria during their growth. Depending on the concentration and redox potential of the produced aldehydes, the reduction of Tollens’ reagent will be a critical step in differentiating between bacteria species. A photograph captures the colorimetric response, and then the RGB values are extracted with an image analysis algorithm designed and presented here. Our results show clear chemical discrimination between five plant pathogenic bacteria after 3 h of incubation in the sensor; no misclassification was observed after this incubation time using hierarchical cluster analysis and linear discrimination analysis. In an experimental model, the sensor correctly classified Pseudomonas savastanoi pv. phaseolicola isolated from halo blight symptomatic leaves and distinguished between other fluorescent bacteria isolated from bean leaves. In addition, the image analysis algorithm presented here can improve RGB extraction due to removing interferences in the sensor substrate compared with the already reported RGB color extraction methods.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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