Abstract A novel treatment method, consisting of pea-gravel with a marine coating supplemented with alkyldimethylbenzylammonium chloride (ADBAC or benzalkonium chloride), has been examined for its antimicrobial performance and coating stability in aqueous environments. Initial column studies examining the porous media's ability to reduce bacterial loads in heating, ventilation, and air conditioning (HVAC) water found average reductions of 94% from pre-flush levels (106 colony forming unit (CFU)/mL) when assessed with R2A spread plates and 83% reductions with SimPlates. There was no observed statistical difference between the average of pre- and post-flush waters from four tests of the media without ADBAC. Taxonomic identification, by 16S rRNA gene sequencing, of colonies drawn from pre- and post-ABDAC R2A plates showed similarities with taxa observed in high frequency from prior cultivation-independent surveys of other cooling tower systems. With this proof of concept, two versions of the media were evaluated for potential coating components released during aqueous exposure. Neither released measurable volatile organic compounds (VOC) components, but one did release bisphenol A and ABDAC compounds. Subsequent column tests of the more durable coating were conducted using cultures of interest in industrial water and demonstrated significant reductions in neutralized post-column Enterococcus faecalis samples and near complete loss of Legionella pneumophila in non-neutralized fluids, but lower reductions in Pseudomonas aeruginosa.
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VOC-Net: A Deep Learning Model for the Automated Classification of Rotational THz Spectra of Volatile Organic Compounds
Conventional black box machine learning (ML) algorithms for gas-phase species identification from THz frequency region absorption spectra have been reported in the literature. While the robust classification performance of such ML models is promising, the black box nature of these ML tools limits their interpretability and acceptance in application. Here, a one-dimensional convolutional neural network (CNN), VOC-Net, is developed and demonstrated for the classification of absorption spectra for volatile organic compounds (VOCs) in the THz frequency range, specifically from 220 to 330 GHz where prior experimental data is available. VOC-Net is trained and validated against simulated spectra, and also demonstrated and tested against experimental spectra. The performance of VOC-Net is examined by the consideration of confusion matrices and receiver-operator-characteristic (ROC) curves. The model is shown to be 99+% accurate for the classification of simulated spectra and 97% accurate for the classification of noisy experimental spectra. The model’s internal logic is examined using the Gradient-weighted Class Activation Mapping (Grad-CAM) method, which provides a visual and interpretable explanation of the model’s decision making process with respect to the important distinguishing spectral features.
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- Award ID(s):
- 1851291
- NSF-PAR ID:
- 10400275
- Date Published:
- Journal Name:
- Applied Sciences
- Volume:
- 12
- Issue:
- 17
- ISSN:
- 2076-3417
- Page Range / eLocation ID:
- 8447
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/app12178447
