Low-cost, open-source cell culture chamber for regulating physiologic oxygen levels.

The physiological oxygen levels for several mammalian cell types in vivo are considered to be hypoxic (low oxygen tension), but the vast majority of in vitro mammalian cell culture is conducted at atmospheric oxygen levels of around 21%. In order to understand the impact of low oxygen environments on cells, oxygen levels need to be regulated during in vitro culture. Two common methods for simulating a hypoxic environment are through the regulation of gas composition or chemical induction. Chemically mimicking hypoxia can have adverse effects such as reducing cell viability, making oxygen regulation in cell culture chambers crucial for long-term culture. However, oxygen-regulating cell culture incubators and commercial hypoxia chambers may not always be a viable option due to cost and limited customization. Other low-cost chambers have been developed, but they tend to lack control systems or are fairly small scale. Thus, the objective of this project was to design and develop a low-cost, open-source, controllable, and reproducible hypoxia chamber that can fit inside a standard cell culture incubator. This design allows for the control of O2 between 1 and 21%, while maintaining CO2 levels at 5%, as well as monitoring of temperature, pressure, and relative humidity. Testing showed our hypoxia chamber was able to maintain CO2 levels at 5% and hypoxic O2 levels at 1% and 5% for long-term cell culture. This simple and easy-to-manufacture design uses off the shelf components, and the total material cost was $832.47 (USD).

Identification of persistent oil residues in Prince William Sound, Alaska using rapid spectroscopic techniques.

Spectroscopic techniques including X-ray fluorescence (XRF) and attenuated total reflectance - Fourier transform infrared spectroscopy (ATR-FTIR) are used to examine oil residues persisting on shorelines in Prince William Sound that originate from the 1989 Exxon Valdez oil spill and oil released as a consequence of the 1964 Great Alaska earthquake. When coupled to classification models, ATR-FTIR and XRF spectral data can be used to distinguish between the two sources of oil with 92% and 86% success rates for the two techniques respectively. Models indicate that the ATR-FTIR data used to determine oil source includes the CO stretch, the twisting-scissoring of the CH2 group, and the CC stretch. For XRF data, decision tree models primarily utilize the abundance of nickel and zinc present in the oil as a means to classify source. This approach highlights the utility of rapid, field-based spectroscopic techniques to distinguish different inputs of oil to coastal environments.

Quantum cascade laser-based reflectance spectroscopy: a robust approach for the classification of plastic type.

The identification of plastic type is important for environmental applications ranging from recycling to understanding the fate of plastics in marine, atmospheric, and terrestrial environments. Infrared reflectance spectroscopy is a powerful approach for plastics identification, requiring only optical access to a sample. The use of visible and near-infrared wavelengths for plastics identification are limiting as dark colored plastics absorb at these wavelengths, producing no reflectance spectra. The use of mid-infrared wavelengths instead enables dark plastics to be identified. Here we demonstrate the capability to utilize a pulsed, widely-tunable (5.59 - 7.41 µm) mid-infrared quantum cascade laser, as the source for reflectance spectroscopy, for the rapid and robust identification of plastics. Through the application of linear discriminant analysis to the resulting spectral data set, we demonstrate that we can correctly classify five plastic types: polyethylene terephthalate (PET), high density polyethylene (HDPE), low density polyethylene (LDPE), polypropylene (PP), and polystyrene (PS), with a 97% accuracy rate.

Rapid Identification of Marine Plastic Debris via Spectroscopic Techniques and Machine Learning Classifiers.

To advance our understanding of the environmental fate and transport of macro- and micro-plastic debris, robust and reproducible methods, technologies, and analytical approaches are necessary for in situ plastic-type identification and characterization. This investigation compares four spectroscopic techniques: attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR), near-infrared (NIR) reflectance spectroscopy, laser-induced breakdown spectroscopy (LIBS), and X-ray fluorescence (XRF) spectroscopy, coupled to seven classification methods, including machine learning classifiers, to determine accuracy for identifying type of both consumer plastics and marine plastic debris (MPD). With machine learning classifiers, consumer plastic types were identified with 99, 91, 97, and 70% success rates for ATR-FTIR, NIR reflectance spectroscopy, LIBS, and XRF, respectively. The classification of MPD had similar or lower success rates, likely arising from alterations to the plastic from environmental weathering processes with success rates of 99, 81, 76, and 66% for ATR-FTIR, NIR reflectance spectroscopy, LIBS, and XRF, respectively. Success rates indicate that ATR-FTIR, NIR reflectance spectroscopy, and LIBS coupled with machine learning classifiers can be used to identify both consumer and environmental plastic samples.

Chemical characterization of natural and anthropogenic-derived oil residues on Gulf of Mexico beaches.

Oil residues originating from the Deepwater Horizon (DWH) incident persist on Gulf of Mexico beaches alongside oil from offshore industrial activity, natural seepage, and asphalt from parking lots and roads. To determine the primary differences in the chemical composition of these oil residues, a variety of samples were collected from beaches from Florida to Alabama over a two-year period from 2015 to 2017. Bulk chemical characteristics of the oil residues were examined via gas chromatography with flame ionization detection (GC-FID) and mass spectrometry (GC-MS), as well as thin layer chromatography with flame ionization detection (TLC-FID), and attenuated total reflectance Fourier transform infrared spectroscopy (ATR FT-IR). These bulk chemical analyses revealed features unique to the different sample types, expanding our understanding of the chemical composition and variability of persistent oil residues, and providing a means to detect and monitor their long-term fate in the coastal environment.