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The adsorption of foulants on photocatalytic nanoparticles can suppress their reactivity in water treatment applications by scavenging reactive species at the photocatalyst surface, screening light, or competing for surface sites. These inhibitory effects are commonly modeled using the Langmuir-Hinshelwood model, assuming that adsorbed layer compositions follow Langmuirian (equilibrium) competitive adsorption. However, this assumption has not been evaluated in complex mixtures of foulants. This study evaluates the photoreactivity of titanium dioxide (TiO2) nanoparticles toward a target compound, phenol, in the presence of two classes of foulants ─ natural organic matter (NOM) and a protein, bovine serum albumin (BSA) ─ and mixtures of the two. Langmuir adsorption models predict that BSA should strongly influence the nanoparticle photoreactivity because of its higher adsorption affinity relative to phenol and NOM. However, model evaluation of the experimental phenol decay rates suggested that neither the phenol nor foulant surface coverages are governed by Langmuirian competitive adsorption. Rather, a reactivity model incorporating kinetic predictions of adsorbed layer compositions (favoring NOM adsorption) outperformed Langmuirian models in providing accurate, unbiased predictions of phenol degradation rates. This research emphasizes the importance of using first-principles models that account for adsorption kinetics when assumptions of equilibrium adsorption do not apply. 

Microbial communities, and their ecological importance, have been investigated in several habitats. However, so far, most studies could not describe the closest microbial interactions and their functionalities. This study investigates the co-occurring interactions between fungi and bacteria in plant rhizoplanes and their potential functions. The partnerships were obtained using fungal-highway columns with four plant-based media. The fungi and associated microbiomes isolated from the columns were identified by sequencing the ITS (fungi) and 16S rRNA genes (bacteria). Statistical analyses including Exploratory Graph and Network Analysis were used to visualize the presence of underlying clusters in the microbial communities and evaluate the metabolic functions associated with the fungal microbiome (PICRUSt2). Our findings characterize the presence of both unique and complex bacterial communities associated with different fungi. The results showed that Bacillus was associated as exo-bacteria in 80 % of the fungi but occurred as putative endo-bacteria in 15 %. A shared core of putative endo-bacterial genera, potentially involved in the nitrogen cycle was found in 80 % of the isolated fungi. The comparison of potential metabolic functions of the putative endo- and exo-communities highlighted the potential essential factors to establish an endosymbiotic relationship, such as the loss of pathways associated with metabolites obtained from the host while maintaining pathways responsible for bacterial survival within the hypha.