Academic Journal

Automated Prediction of Bacterial Exclusion Areas on SEM Images of Graphene–Polymer Composites

التفاصيل البيبلوغرافية
العنوان: Automated Prediction of Bacterial Exclusion Areas on SEM Images of Graphene–Polymer Composites
المؤلفون: Shadi Rahimi, Teo Lovmar, Alexandra Aulova, Santosh Pandit, Martin Lovmar, Sven Forsberg, Magnus Svensson, Roland Kádár, Ivan Mijakovic
المصدر: Nanomaterials, Vol 13, Iss 10, p 1605 (2023)
بيانات النشر: MDPI AG, 2023.
سنة النشر: 2023
المجموعة: LCC:Chemistry
مصطلحات موضوعية: antibacterial, bacterial exclusion area, graphene flakes, algorithm, vertical, Chemistry, QD1-999
الوصف: To counter the rising threat of bacterial infections in the post-antibiotic age, intensive efforts are invested in engineering new materials with antibacterial properties. The key bottleneck in this initiative is the speed of evaluation of the antibacterial potential of new materials. To overcome this, we developed an automated pipeline for the prediction of antibacterial potential based on scanning electron microscopy images of engineered surfaces. We developed polymer composites containing graphite-oriented nanoplatelets (GNPs). The key property that the algorithm needs to consider is the density of sharp exposed edges of GNPs that kill bacteria on contact. The surface area of these sharp exposed edges of GNPs, accessible to bacteria, needs to be inferior to the diameter of a typical bacterial cell. To test this assumption, we prepared several composites with variable distribution of exposed edges of GNP. For each of them, the percentage of bacterial exclusion area was predicted by our algorithm and validated experimentally by measuring the loss of viability of the opportunistic pathogen Staphylococcus epidermidis. We observed a remarkable linear correlation between predicted bacterial exclusion area and measured loss of viability (R2 = 0.95). The algorithm parameters we used are not generally applicable to any antibacterial surface. For each surface, key mechanistic parameters must be defined for successful prediction.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2079-4991
Relation: https://www.mdpi.com/2079-4991/13/10/1605; https://doaj.org/toc/2079-4991
DOI: 10.3390/nano13101605
URL الوصول: https://doaj.org/article/01a5f3ce5e904b42a78cb80990804105
رقم الانضمام: edsdoj.01a5f3ce5e904b42a78cb80990804105
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:20794991
DOI:10.3390/nano13101605