Quantitative Criterion to Predict Cell Adhesion by Identifying Dominant Interaction between Microorganisms and...

Cell adhesion is a widespread phenomenon with relevance in biotechnology, materials science, and environmental engineering. Prior research has shown that adhesion involves complex interactions between microorganisms and surfaces. However, no prior work had resolved how to distinguish between dominant interaction types in a quantitative way. Established knowledge includes the roles of electrostatic and acid-base forces, but uncertainty remained about how to predict which interaction dominates in specific cases. This gap motivated the need for a predictive model that integrates both types of interactions. No prior work had resolved how to combine electron-donor characteristics with ζ potentials in a unified framework. That uncertainty drove the development of a new quantitative criterion. This gap motivated the need for a predictive model that integrates both types of interactions. No prior work had resolved how to combine electron-donor characteristics with ζ potentials in a unified framework.

The aim of this study was to develop a quantitative criterion to predict cell adhesion by identifying the dominant interaction type. The specific problem addressed is the lack of a unified model to distinguish between acid-base and electrostatic interactions in microbial adhesion. The motivation comes from the need to improve predictions in biotechnology and materials science applications. The researchers propose a framework based on electron-donor characteristics and ζ potentials. This approach allows for the comparison of acid-base and electrostatic interactions in a single model. The study focuses on microorganisms such as microalgae, bacteria, and fungi. The researchers propose a framework based on electron-donor characteristics and ζ potentials. This approach allows for the comparison of acid-base and electrostatic interactions in a single model.

The study used a theoretical framework combining electron-donor characteristics and ζ potentials. The researchers compared two expressions involving γ_mv^-, γ_sv^-, ζ_m, and ζ_s. Experimental validation was performed using microalgae, bacteria, and fungi on various surfaces. Data from literature studies were also incorporated for verification. The dominant interaction was determined by comparing the two expressions. The model was tested across multiple microorganism-surface combinations. Data from literature studies were also incorporated for verification. The dominant interaction was determined by comparing the two expressions.

The results showed that adhesion behavior depends on the dominant interaction type. When acid-base interactions were dominant, adhesion decreased with increasing [Formula: see text]. When electrostatic interactions were dominant, adhesion decreased with increasing (ζ_m + ζ_s)². The model was validated using microalgae, bacteria, and fungi. Experimental data confirmed the predicted trends. The criterion was tested on surfaces with varying electron-donor characteristics. The model successfully distinguished between acid-base and electrostatic dominance. The criterion was tested on surfaces with varying electron-donor characteristics. The model successfully distinguished between acid-base and electrostatic dominance.

The authors concluded that the proposed criterion allows for the prediction of cell adhesion based on dominant interaction types. The model integrates acid-base and electrostatic interactions into a single framework. The results demonstrated the criterion's applicability across multiple microorganism-surface systems. The study confirmed that adhesion trends align with the predicted behavior. The researchers propose that this approach improves the predictability of microbial adhesion. The model was validated using both experimental and literature data. The study confirmed that adhesion trends align with the predicted behavior. The researchers propose that this approach improves the predictability of microbial adhesion.