A cost-effective and facile technique for realizing fabric based microfluidic channels using beeswax and PVC stencils.

Microfluidic channels fabricated over fabrics or papers have the potential to find substantial application in the next generation of wearable healthcare monitoring systems. The present work focuses on the fabrication procedures that can be used to obtain practically realizable fabric-based microfluidic channels (µFADs) utilizing patterning masks and wax, unlike conventional printing techniques. In this study, comparative analysis was used to differentiate channels obtained using different masking tools for channel patterning as well as different wax materials as hydrophobic barriers. Drawbacks of the conventional tape and candle wax technique were noted and a novel approach was used to create microfluidic channels through a facile and simple masking technique using PVC clear sheets as channel stencils and beeswax as the channel barriers. The resulting fabric based microfluidic channels with varying widths as well as complex microchannel, microwell, and micromixer designs were investigated and a minimum channel width resolution of 500 µm was successfully obtained over cotton based fabrics. Thereafter, the PVC clear sheet-beeswax based microwells were successfully tested to confine various organic and inorganic samples indicating vivid applicability of the technique. Finally, the microwells were used to make a simple and facile colorimetric assay for glucose detection and demonstrated effective detection of glucose levels from 10 mM to 50 mM with significant color variation using potassium iodide as the coloring agent. The above findings clearly suggest the potential of this alternative technique for making low-cost and practically realizable fabric based diagnostic devices (µFADs) in contrast to the other approaches that are currently in use.

Computational Fluid Dynamics Simulation on Thermal Performance of Al/Al2O3/SWCNT Nanocoolants for Turning Operations.

The objective of this study is to numerically investigate the thermal performance of cutting fluids dispersed with nanoparticles for effective heat removal during turning operations. The simulations are performed using Ansys Fluent software, and the problem is modelled as a three-dimensional turbulent incompressible single-phase flow. The computational domain consists of a heated cutting tool and work piece, and nanocoolants are sprayed from a nozzle located above the machining zone. The nanocoolants are prepared by mixing mineral oil with nanoparticles of Al2O3 (Aluminium Oxide), Al (Aluminium) and SWCNT (Single Walled Carbon Nanotube). The heat transfer performances of different nanocoolants are compared by varying the nanoparticle volume fraction (φ) and coolant velocity (Uc) in the range of 2% ≤ φ ≤ 8% and 1 m/s ≤ Uc ≤ 15 m/s, respectively. The results indicated a drastic drop in the cutting tool temperature with an increase in the volume fraction of dispersed nanoparticles and coolant velocity. The increase in volume fraction decreases the average cutting tool temperature by 25.65% and also enhances the average heat transfer rate by 25.43%. It is additionally observed that SWCNT nanocoolants exhibited a superior thermal performance and heat removal rate compared with Al and Al2O3 nanocoolants. The analysed numerical results are validated and are in good accordance with the benchmark results validated from literature.

A comprehensive review of approaches to detect fatigue using machine learning techniques.

In the past decades, there have been numerous advancements in the field of technology. This has led to many scientific breakthroughs in the field of medical sciences. In this, rapidly transforming world we are having a difficult time and the problem of fatigue is becoming prevalent. So, this study aimed to understand what is fatigue, its repercussions, and techniques to detect it using machine learning (ML) approaches. This paper introduces, discusses methods and recent advancements in the field of fatigue detection. Further, we categorized the methods that can be used to detect fatigue into four diverse groups, that is, mathematical models, rule-based implementation, ML, and deep learning. This study presents, compares, and contrasts various algorithms to find the most promising approach that can be used for the detection of fatigue. Finally, the paper discusses the possible areas for improvement.

Oligomer Sensor Nanoarchitectonics for "Turn-On" Fluorescence Detection of Cholesterol at the Nanomolar Level.

Sensitive and rapid monitoring of cholesterol levels in the human body are highly desirable as they are directly related to the diagnosis of cardiovascular diseases. By using the nanoarchitectonic approach, a novel fluorescent conjugated oligofluorene (OFP-CD) functionalized with ß-cyclodextrin (ß-CD) was assembled for "Turn-On" fluorescence sensing of cholesterol. The appended ß-CD units in OFP-CD enabled the forming of host-guest complexes with dabsyl chloride moieties in water, resulting in fluorescence quenching of the oligofluorene through intermolecular energy transfer. In the presence of cholesterol molecules, a more favorable host-guest complex with stoichiometry 1 cholesterol: 2 ß-CD units was formed, replacing dabsyl chloride in ß-CD's cavities. This process resulted in fluorescence recovery of OFP-CD, owing to disruption of energy transfer. The potential of this nanoarchitectonic system for "Turn-On" sensing of cholesterol was extensively studied by fluorescence spectroscopy. The high selectivity of the sensor for cholesterol was demonstrated using biologically relevant interfering compounds, such as carbohydrates, amino acids, metal ions, and anions. The detection limit (LOD value) was as low as 68 nM, affirming the high sensitivity of the current system.