Here we investigated the use of machine learning (ML) techniques to “derive” an implicit solvent model based on the average solvent environment configurations from explicit solvent molecular dynamics (MD) simulations.
Western people now spend close to 90% of their time indoors, one-quarter of which occurs at their place of employment. As such, interactions between employees and the workplace built environment have significant potential impact on employee health and safety. However, the range of workers’ daily chemical exposures is still poorly understood. Likewise, the influence of workers themselves and of worker behavior on the chemical composition of the workplace is still unknown. In this case study, we used untargeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) to compare the chemical signatures of three different types of workplaces: scientific research buildings, office buildings, and one mixed-purpose building. Our results identified differential signatures of public building surfaces based on building purpose, sampling location and surface materials. Overall, these results are helping define the influence of human behavior on the workplace chemical environment and identify the chemical hazards to which people are exposed throughout their workday.HighlightsImplementation of untargeted liquid chromatography-tandem mass spectrometry to study workplace chemical exposures.Shared chemical signatures were identified based on building purpose.Differential chemical signatures were identified based on surface material and sampling location.Annotated molecules include pharmaceuticals, illicit drugs, food chemicals, constituents of paints and stains, and cleaning products.
In recent years, considerable progress
has been made on the synthetic
chemistry and antibacterial application of tricobalt tetroxide (Co3O4) nanomaterials. However, the current approaches
to designing and synthesizing Co3O4 nanomaterials
are complicated and hard to manipulate. Herein, we developed a one-pot
strategy to synthesize Co3O4 nanowires at room
temperature with an antibacterial activity. The synthesis process
relied on the use of engineered bacterial flagella as a biotemplate,
which were genetically modifiable protein nanofibers naturally attached
to bacteria for assisting their swimming. We found that the flagella
displaying negatively charged peptides (E10 and E20) effectively induced
the nucleation of Co3O4 nanoparticles from a
cobalt chloride (CoCl2) precursor solution on their surface
to form polycrystalline nanowires, with the E20-flagella being more
effective than the E10-flagella. However, the wildtype flagella or
those displaying neutral (G10) or positively charged (K5) peptides
did not effectively induce the Co3O4 nucleation
on the flagella. A mechanism investigation discovered that an amorphous
phase of Co3O4 was first formed rapidly on the
E20-flagella, followed by a crystallization process with both the
good crystallinity and nanowire water-dispersibility reached in 2
h. We also found that the E20-flagella formed the Co3O4 nanowires with the good crystallinity at a precursor solution
of 1 mM. The E20-flagella-templated Co3O4 nanowires,
synthesized using the optimal mineralization conditions (2 h and 1
mM CoCl2), showed the most effective killing of Gram-negative
bacteria such as Escherichia coli.
This work suggests that flagella with tunable peptide sequences are
biotemplates for forming antibacterial nanowires at environmentally
benign conditions.
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