A new catalyst layer based on in situ pore generation of sulfonated poly(ether ether ketone) for PEMFC

A new concept of in situ pore generation to reduce water flooding in cathode catalyst layer (CCL) of polymer electrolyte membrane fuel cell (PEMFC) is proposed with the introduction of water soluble poly(ethylene glycol) (PEG) as a porogen to CCL based on sulfonated poly(ether ether ketone) (sPEEK). In this new type of CCL, PEG is directly removed by water produced during the cathode reaction. The new CCL exhibited much higher cell performance especially in mass transport limitation region compared to the pristine sPEEK-CCL. In addition, the presence of PEG in the new CCL lowered the glass transition temperature of the sPEEK binder, and it could improve the transference of catalyst layer onto the polymer electrolyte membrane.     

Rate constants for the quenching of metastable O2(1Σ) molecules

Singlet molecular oxygen, O₂(¹Σ), is one of the important intermediate species in the atmospheres of Earth, Mars, and Venus. To elucidate the chemistry of this excited molecular oxygen, a series of kinetic measurements have been undertaken using the flow-discharge/optical-emission technique. By monitoring the characteristic emission (762 nm for ¹Σ), the quenching rates for several important molecules have been obtained at room temperature. The following table summarizes measurements.

Yields of O2(b1Σ) from reactions of HO2

A discharge-flow apparatus with resonance fluorescence and chemiluminescence detection has been used to monitor O₂(b ¹σ) production from several reactions of the HO₂ radical at 300 K and 1-torr total pressure. O₂(b), HO₂, and OH were observed when F atoms were added to H₂O₂ in the gas phase. Signal strengths of O₂(b) were proportional to initial concentrations of H₂O₂ and HO₂. These observations were analyzed by using a simple three step mechanism and a more complete computer simulation with 22 reaction steps. The results indicate that the F + HO₂ reaction yields O₂(b) with an efficiency of (3.6 ± 1.4) × 10^?3. By monitoring [O₂(b)] and [HO₂] upon addition of an excess second reactant to HO₂, O₂(b) yields from the reactions of HO₂ with O, Cl, D, H, and OH were found to be <1 × 10^?2, <5 × 10^?4, <2 × 10^?3, <8 × 10^?3, and <1 × 10^?3, respectively. Yields of O₂(b) from the HO₂ ± HO₂ reaction were found to be less than 3 × 10^?2.     

Automated oral cancer identification using histopathological images: a hybrid feature extraction paradigm

Oral cancer (OC) is the sixth most common cancer in the world. In India it is the most common malignant neoplasm. Histopathological images have widely been used in the differential diagnosis of normal, oral precancerous (oral sub-mucous fibrosis (OSF)) and cancer lesions. However, this technique is limited by subjective interpretations and less accurate diagnosis. The objective of this work is to improve the classification accuracy based on textural features in the development of a computer assisted screening of OSF. The approach introduced here is to grade the histopathological tissue sections into normal, OSF without Dysplasia (OSFWD) and OSF with Dysplasia (OSFD), which would help the oral onco-pathologists to screen the subjects rapidly. The biopsy sections are stained with H&E. The optical density of the pixels in the light microscopic images is recorded and represented as matrix quantized as integers from 0 to 255 for each fundamental color (Red, Green, Blue), resulting in a M×N×3 matrix of integers. Depending on either normal or OSF condition, the image has various granular structures which are self similar patterns at different scales termed "texture". We have extracted these textural changes using Higher Order Spectra (HOS), Local Binary Pattern (LBP), and Laws Texture Energy (LTE) from the histopathological images (normal, OSFWD and OSFD). These feature vectors were fed to five different classifiers: Decision Tree (DT), Sugeno Fuzzy, Gaussian Mixture Model (GMM), K-Nearest Neighbor (K-NN), Radial Basis Probabilistic Neural Network (RBPNN) to select the best classifier. Our results show that combination of texture and HOS features coupled with Fuzzy classifier resulted in 95.7% accuracy, sensitivity and specificity of 94.5% and 98.8% respectively. Finally, we have proposed a novel integrated index called Oral Malignancy Index (OMI) using the HOS, LBP, LTE features, to diagnose benign or malignant tissues using just one number. We hope that this OMI can help the clinicians in making a faster and more objective detection of benign/malignant oral lesions.     

Mechanical behavior of bulk amorphous alloys reinforced by ductile particles at cryogenic temperatures

The mechanical behavior of Zr-based bulk amorphous alloy composites (BAACs) was investigated at 77 K. The 5 vol. % Ta-BAAC maintained large plastic strains of approximately 13% with a 16% strength increase, when compared with that at 298 K. The interaction between shear bands and particles shows that shear extension in particles has limited penetration, and shear bands build up around particles. In addition to on the failure surface of the amorphous matrix, molten characteristics were also found on the surface of sheared particles. Pair distribution function studies were performed to understand the mechanical behavior.     

A tiling proof of Euler’s Pentagonal Number Theorem and generalizations

The Ramanujan Journal - In two papers, Little and Sellers introduced an exciting new combinatorial method for proving partition identities which is not directly bijective. Instead, they consider...     

New Equation for Predicting Pipe Friction Coefficients Using the Statistical Based Entropy Concepts

In general, this new equation is significant for designing and operating a pipeline to predict flow discharge. In order to predict the flow discharge, accurate determination of the flow loss due to pipe friction is very important. However, existing pipe friction coefficient equations have difficulties in obtaining key variables or those only applicable to pipes with specific conditions. Thus, this study develops a new equation for predicting pipe friction coefficients using statistically based entropy concepts, which are currently being used in various fields. The parameters in the proposed equation can be easily obtained and are easy to estimate. Existing formulas for calculating pipe friction coefficient requires the friction head loss and Reynolds number. Unlike existing formulas, the proposed equation only requires pipe specifications, entropy value and average velocity. The developed equation can predict the friction coefficient by using the well-known entropy, the mean velocity and the pipe specifications. The comparison results with the Nikuradse’s experimental data show that the R2 and RMSE values were 0.998 and 0.000366 in smooth pipe, and 0.979 to 0.994 or 0.000399 to 0.000436 in rough pipe, and the discrepancy ratio analysis results show that the accuracy of both results in smooth and rough pipes is very close to zero. The proposed equation will enable the easier estimation of flow rates.