A non-invasive capacitive sensor to investigate the Leidenfrost phenomenon:a proof of concept study

A volatile sessile liquid droplet or a sublimating solid manifests levitation on its own vapor when placed on a sufficiently heated surface, illustrating the Leidenfrost phenomenon. In this study, we introduce a non-invasive capacitance method for investigating this phenomenon, offering a potentially simpler alternative to existing optical techniques. The designed sensor features in-plane miniaturized electrodes forming a double-comb structure, also known as an interdigitated capacitor. Initially, the sensor’s capacitance is characterized for various distances between the sensor and a dielectric material. The influence of the sensor substrate material and the spacing between the electrodes on the sensor’s capacitance is also investigated. To demonstrate the feasibility of the method, a sublimating dry ice pellet is placed on the capacitive sensor, and its performance is evaluated. We present results for the dimensionless vapor layer thickness and the pellet’s lifetime at different substrate temperatures, derived from the capacitance output. The results are compared with Optical Coherence Tomography (OCT) data, serving as a benchmark. While the temporal evolution of the sensor’s output, variation in the dimensionless vapor layer thickness, and the lifetime of the dry ice pellet align with expected results from OCT, notable quantitative deviations are observed. These deviations are attributed to practical experimental limitations rather than shortcoming in the sensor’s working principle. Although this necessitates further investigation, the methodology presented in this paper can potentially serve as an alternative for the detection and measurement of Leidenfrost vapor layers.</p

EGFR Mutations in Indian Lung Cancer Patients: Clinical Correlation and Outcome to EGFR Targeted Therapy

L

Deep learning based automated epidermal growth factor receptor and anaplastic lymphoma kinase status prediction of brain metastasis in non-small cell lung cancer

Aim: The aim of this study was to investigate the feasibility of developing a deep learning (DL) algorithm for classifying brain metastases from non-small cell lung cancer (NSCLC) into epidermal growth factor receptor (EGFR) mutation and anaplastic lymphoma kinase (ALK) rearrangement groups and to compare the accuracy with classification based on semantic features on imaging. Methods: Data set of 117 patients was analysed from 2014 to 2018 out of which 33 patients were EGFR positive, 43 patients were ALK positive and 41 patients were negative for either mutation. Convolutional neural network (CNN) architecture efficient net was used to study the accuracy of classification using T1 weighted (T1W) magnetic resonance imaging (MRI) sequence, T2 weighted (T2W) MRI sequence, T1W post contrast (T1post) MRI sequence, fluid attenuated inversion recovery (FLAIR) MRI sequences. The dataset was divided into 80% training and 20% testing. The associations between mutation status and semantic features, specifically sex, smoking history, EGFR mutation and ALK rearrangement status, extracranial metastasis, performance status and imaging variables of brain metastasis were analysed using descriptive analysis [chi-square test (χ2)], univariate and multivariate logistic regression analysis assuming 95% confidence interval (CI). Results: In this study of 117 patients, the analysis by semantic method showed 79.2% of the patients belonged to ALK positive were non-smokers as compared to double negative groups (P = 0.03). There was a 10-fold increase in ALK positivity as compared to EGFR positivity in ring enhancing lesions patients (P = 0.015) and there was also a 6.4-fold increase in ALK positivity as compared to double negative groups in meningeal involvement patients (P = 0.004). Using CNN Efficient Net DL model, the study achieved 76% accuracy in classifying ALK rearrangement and EGFR mutations without manual segmentation of metastatic lesions. Analysis of the manually segmented dataset resulted in improved accuracy of 89% through this model. Conclusions: Both semantic features and DL model showed comparable accuracy in classifying EGFR mutation and ALK rearrangement. Both methods can be clinically used to predict mutation status while biopsy or genetic testing is undertaken

Sublimation Kinetics of Carbon Dioxide

The objective of the research outlined in this thesis is to comprehend the fundamental phenomenon of dry ice sublimation, with a particular focus on its behavior in a gaseous environment, on a hot solid surface, and in an insulated container.In Part I (Chapters 2-3), we study the sublimation of a dry ice sphere in a gaseous environment under varying far-field conditions. Chapter 2 demonstrates that for a given far-field pressure, the dry ice sublimation temperature decreases significantly from the commonly quoted value of 194.6 K as the far-field CO2 concentration reduces due to sublimative cooling. Furthermore, a decrease in far-field pressure for a given CO2 concentration further lowers the sublimation temperature due to increased diffusion of CO2 vapor away from the dry ice surface. Chapter 3 qualitatively compares the nature of density gradients of the CO2 vapor near the phase-changing interface with the light intensity variations observed via Schlieren imaging. Both parameters progressively increase in a similar manner along the curvature of the dry ice sphere, peaking at the horizontal plane before decreasing towards the bottom. Additionally, these parameters decrease exponentially along the radial direction at the sphere's horizontal plane.In Part II (Chapters 4-5), we investigate the Leidenfrost phenomenon for a disc-shaped dry ice pellet. Chapter 5 employs the Optical Coherence Tomography (OCT) technique to reveal the spatial and temporal evolution profiles of the vapor layer beneath the dry ice pellet. Unlike Leidenfrost puddles, the vapor layer beneath the pellet is found to be approximately flat, increasing over time until the near end of its lifetime. As a simpler alternative to existing optical techniques, Chapter 6 demonstrates the use of a non-invasive capacitive method to investigate the Leidenfrost phenomenon. This method's results for vapor layer thickness and pellet lifetime were validated by comparing them to those obtained from OCT for different substrate temperatures.In Part III (Chapter 6), we experimentally and numerically evaluate the sublimation rate of dry ice pellets placed inside an insulated container—a scenario directly relevant to cold-chain logistics applications. It is shown that the contact area between the inner walls of the container and the pellets significantly contributes to the total heat transfer rate for the majority of the dry ice’s lifetime. Additionally, the dry ice sublimation rate can be considerably reduced by lowering the surface emissivity of the inner walls of the insulated container