Carbon dots with tunable third-order nonlinear coefficient instructed by machine learning

Machine Learning Tools to Assist the Synthesis of Antibacterial Carbon Dots

Introduction

The emergence and rapid spread of multidrug-resistant bacteria (MRB) caused by the excessive use of antibiotics and the development of biofilms have been a growing threat to global public health. Nanoparticles as substitutes for antibiotics were proven to possess substantial abilities for tackling MRB infections via new antimicrobial mechanisms. Particularly, carbon dots (CDs) with unique (bio)physicochemical characteristics have been receiving considerable attention in combating MRB by damaging the bacterial wall, binding to DNA or enzymes, inducing hyperthermia locally, or forming reactive oxygen species.

Methods

Herein, how the physicochemical features of various CDs affect their antimicrobial capacity is investigated with the assistance of machine learning (ML) tools.

Results

The synthetic conditions and intrinsic properties of CDs from 121 samples are initially gathered to form the raw dataset, with Minimum inhibitory concentration (MIC) being the output. Four classification algorithms (KNN, SVM, RF, and XGBoost) are trained and validated with the input data. It is found that the ensemble learning methods turn out to be the best on our data. Also, ε-poly(L-lysine) CDs (PL-CDs) were developed to validate the practical application ability of the well-trained ML models in a laboratory with two ensemble models managing the prediction.

Discussion

Thus, our results demonstrate that ML-based high-throughput theoretical calculation could be used to predict and decode the relationship between CD properties and the anti-bacterial effect, accelerating the development of high-performance nanoparticles and potential clinical translation.

A Framework for Biosensors Assisted by Multiphoton Effects and Machine Learning

The ability to interpret information through automatic sensors is one of the most important pillars of modern technology. In particular, the potential of biosensors has been used to evaluate biological information of living organisms, and to detect danger or predict urgent situations in a battlefield, as in the invasion of SARS-CoV-2 in this era. This work is devoted to describing a panoramic overview of optical biosensors that can be improved by the assistance of nonlinear optics and machine learning methods. Optical biosensors have demonstrated their effectiveness in detecting a diverse range of viruses. Specifically, the SARS-CoV-2 virus has generated disturbance all over the world, and biosensors have emerged as a key for providing an analysis based on physical and chemical phenomena. In this perspective, we highlight how multiphoton interactions can be responsible for an enhancement in sensibility exhibited by biosensors. The nonlinear optical effects open up a series of options to expand the applications of optical biosensors. Nonlinearities together with computer tools are suitable for the identification of complex low-dimensional agents. Machine learning methods can approximate functions to reveal patterns in the detection of dynamic objects in the human body and determine viruses, harmful entities, or strange kinetics in cells.