Publications

In the area of coating development, it is extremely difficult to find a substitute for bisphenol A diglycidyl ether (DGEBA), the classical petroleum-based raw material used for the formulation of epoxy thermosets. This epoxy resin offers fast curing reaction with several hardeners and the best thermal and chemical resistance properties for applications in coatings and adhesive technologies. In this work, a new biobased epoxy, derived from poly(limonene carbonate) oxide (PLCO), was combined with polyetheramine and polyamineamide curing agents, offering a spectrum of thermal and mechanical properties, superior to DGEBA-based thermosets. The best formulation was found to be a combination of PLCO and a commercial curing agent (Jeffamine) in a stoichiometric 1:1 ratio. Although PLCO is a solid due to its high molecular weight, it was possible to create a two-component partially biobased epoxy paint without the need of volatile organic compounds (i.e., solvent-free formulation), intended for use in coating technology to partially replace DGEBA-based thermosets.

JTD Keywords: acid, adhesion, epoxy thermoset, mechanical properties, monomer, polycarbonates, polymers, protection, resins, solvent-free paint, thermal properties, Adhesives, Biobased epoxy, Bisphenol-a-diglycidyl ethers, Carbonation, Coating development, Coating technologies, Curing, Curing agents, Epoxy coatings, Epoxy resins, Epoxy thermoset, Epoxy thermosets, Limonene oxide, Mechanical properties, Monoterpenes, Paint, Poly(limonene carbonate) oxide, Solvent free, Solvent-free paint, Thermal properties, Thermosets, Volatile organic compounds

Smoldering fires are characterized by the production of early gas emissions that can include high levels of CO and Volatile Organic Compounds (VOCs) due to pyrolysis or thermal degradation. Nowadays, standalone CO sensors, smoke detectors, or a combination of these, are standard components for fire alarm systems. While gas sensor arrays together with pattern recognition techniques are a valuable alternative for early fire detection, in practice they have certain drawbacks-they can detect early gas emissions, but can show low immunity to nuisances, and sensor time drift can render calibration models obsolete. In this work, we explore the performance of a gas sensor array for detecting smoldering and plastic fires while ensuring the rejection of a set of nuisances. We conducted variety of fire and nuisance experiments in a validated standard fire room (240 m(3)). Using PLS-DA and SVM, we evaluate the performance of different multivariate calibration models for this dataset. We show that calibration models remain predictive after several months, but perfect performance is not achieved. For example, 4 months after calibration, a PLS-DA model provides 100% specificity and 85% sensitivity since the system has difficulties in detecting plastic fires, whose signatures are close to nuisance scenarios. Nevertheless, our results show that systems based on gas sensor arrays are able to provide faster fire alarm response than conventional smoke-based fire alarms. We also propose the use of small-scale fire experiments to increase the number of calibration conditions at a reduced cost. Our results show that this is an effective way to increase the performance of the model, even when evaluated on a standard fire room. Finally, the acquired datasets are made publicly available to the community (doi: 10.5281/zenodo.5643074).

JTD Keywords: Calibration, Chemical sensors, Co2, Early fire, Early fire detection, En-54, Fire alarm, Fire detection, Fire room, Fires, Gas detectors, Gas emissions, Gas sensors, Pattern recognition, Public dataset, Sensor arrays, Sensors array, Signatures, Smoke, Smoke detector, Smoke detectors, Standard fire, Standard fire room, Support vector machines, Temperature, Toxicity, Volatile organic compounds

Background: Sepsis is one of the main causes of death in adult intensive care units. The major drawbacks of the different methods used for its diagnosis and monitoring are their inability to provide fast responses and unsuitability for bedside use. In this study, performed using a rat sepsis model, we evaluate breath analysis with Ion Mobility Spectrometry (IMS) as a fast, portable and non-invasive strategy. Methods: This study was carried out on 20 Sprague-Dawley rats. Ten rats were injected with lipopolysaccharide from Escherichia coli and ten rats were IP injected with regular saline. After a 24-h period, the rats were anaesthetized and their exhaled breaths were collected and measured with IMS and SPME-gas chromatography/mass spectrometry (SPME-GC/MS) and the data were analyzed with multivariate data processing techniques. Results: The SPME-GC/MS dataset processing showed 92% accuracy in the discrimination between the two groups, with a confidence interval of between 90.9% and 92.9%. Percentages for sensitivity and specificity were 98% (97.5–98.5%) and 85% (84.6–87.6%), respectively. The IMS database processing generated an accuracy of 99.8% (99.7–99.9%), a specificity of 99.6% (99.5–99.7%) and a sensitivity of 99.9% (99.8–100%). Conclusions: IMS involving fast analysis times, minimum sample handling and portable instrumentation can be an alternative for continuous bedside monitoring. IMS spectra require data processing with proper statistical models for the technique to be used as an alternative to other methods. These animal model results suggest that exhaled breath can be used as a point-of-care tool for the diagnosis and monitoring of sepsis.

JTD Keywords: Sepsis, Volatile organic compounds, Ion mobility spectrometer, Rat model, Bedside patient systems, Non-invasive detection