Processing operations such as milling, change the chemical structure of rubber. Infrared (IR) spectroscopy and electron spectroscopy for chemical analysis (ESCA) have been used to detect the formation of functional groups during the milling of natural rubber ( N R ) , epoxidised natural rubber ( E N R ) , styrene butadiene rubber ( S B R ) , neoprene ( C R ) , and acrylic rubber ( A R ) . The concentration of carbonyl ( 'CO) groups as detected by IR spectroscopy increases with the time of milling for all the rubbers. The concentration of hydroxyl groups (-OH) in N R and ENR also increases during milling. ESCA spectra of the rubbers before and after milling reveal that the peaks are broader for milled rubber, and that the concentration of oxygen is also greater. In the case of ENR, however, the concentration of epoxide rings decreases and that of furan rings increases with the time of milling.
The COVID-19 pandemic has affected millions and congested healthcare systems globally. Hence an objective severity assessment is crucial in making therapeutic decisions judiciously. Computed Tomography (CT)-scans can provide demarcating features to identify the severity of pneumonia, commonly associated with COVID-19, in the affected lungs. Here, a semi-quantitative severity assessing lung CT image feature is demonstrated for COVID-19 patients. An open-source multi-center Italian database was used, among which 61 cases were incorporated in the study (age 27-86, 71% males) from 27 CT imaging centers. Lesions in the form of opacifications, crazy-paving patterns, and consolidations were heuristically marked and the severity determining feature L_norm was quantified and established to be statistically distinct for the three classes i.e., mild, moderate, and severe (p-value < 0.0001). The thresholds of L_norm for a 3-class classification were determined based on the optimum sensitivity/specificity combination from ROC analyses. The feature L_norm classified the cases in the three severity categories with 89.34% accuracy when L_norm was calculated based on experts' heuristic annotations. Substantial to almost-perfect intra-rater and inter-rater agreements were achieved involving expert and non-expert based evaluations (κ-score 0.79-0.97). We trained machine learning based classification models and showed L_norm alone has a superior diagnostic accuracy over standard image intensity and texture features. Classification accuracy was further increased when L_norm was used to delineate the severe cases from non-severe ones (98.9%) with high sensitivity (97.7%), and specificity (97.49%). Therefore, key highlights of this severity assessment feature are accuracy, lower dependency on expert availability, and wide utility across multiple imaging centers.
Mill processing behavior, with respect to front to back roll (F-B) transition, of polychloroprene and acrylic rubbers has been studied. The critical nip gap at which F-B transition occurs has been determined for these rubbers and their filled compounds (including HAF, MT, silica, and whiting) over a range of friction ratios and temperatures. The nip gap increases with friction ratio and temperature. The theoretical model of Tokita based on dimensional analysis, reveals three conditions corresponding to three types of behavior of various rubbers in drop mill operation. Mill band formation indices corresponding to these behavior types have been defined. These indices can be used to determine the conditions for front roll band formation. The systems investigated satisfy one of the conditions of the model. The mill processing behavior of these systems in a continuous milling operation has also been reported.
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