We report a one-step oxidative method for the recycling of carbon
fiber-reinforced polymer (CFRP) composite waste and the recovery of
carbon fiber and the epoxy resin by treatment with peracetic acid
(PAA) formed in situ from a mixture of acetic acid and hydrogen peroxide.
The surfaces of the recovered carbon fibers were clean, and the tensile
strength was comparable to that of the virgin fibers. A higher resin
decomposition ratio of 97% could be achieved for the epoxy matrix
under mild reaction conditions in comparison to other chemical recycling
processes. ATR-FTIR, MALDI/TOF-MS, GPC, GC-MS, 1H NMR,
and Pyro-GC/MS analyses showed the formation of low molecular weight
oxidation products of amine-cured epoxy resin along with high molecular
weight compounds of high viscosity. A possible reaction mechanism
for the degradation of the epoxy matrix is proposed. All the solvents
used were recovered in pure and reusable form with more than 90% recovery
efficiency. The recovered epoxy was reused along with an adhesive
grade epoxy (2 wt %) with no significant loss of tensile strength.
Almost complete recovery of the recycled products as well as the solvent
along with no gaseous emissions and mild reaction conditions make
this process more environmentally friendly.
Machine learning and data analytics are being increasingly used for quantitative structure property relation (QSPR) applications in the chemical domain where the traditional Edisonian approach towards knowledge-discovery have not been fruitful. The perception of odorant stimuli is one such application as olfaction is the least understood among all the other senses. In this study, we employ machine learning based algorithms and data analytics to address the efficacy of using a data-driven approach to predict the perceptual attributes of an odorant namely the odorant characters (OC) of “sweet” and “musky”. We first analyze a psychophysical dataset containing perceptual ratings of 55 subjects to reveal patterns in the ratings given by subjects. We then use the data to train several machine learning algorithms such as random forest, gradient boosting and support vector machine for prediction of the odor characters and report the structural features correlating well with the odor characters based on the optimal model. Furthermore, we analyze the impact of the data quality on the performance of the models by comparing the semantic descriptors generally associated with a given odorant to its perception by majority of the subjects. The study presents a methodology for developing models for odor perception and provides insights on the perception of odorants by untrained human subjects and the effect of the inherent bias in the perception data on the model performance. The models and methodology developed here could be used for predicting odor characters of new odorants.
We report a neural network model for predicting the electromagnetic response of mesoscale metamaterials as well as generate design parameters for a desired spectral behavior. Our approach entails treating spectral data as time-varying sequences and the inverse problem as a single-input multiple output model, thereby compelling the network architecture to learn the geometry of the metamaterial designs from the spectral data in lieu of abstract features.
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