Preparation of core-shell heterojunction photocatalysts by coating CdS nanoparticles onto Bi4Ti3O12 hierarchical microspheres and their photocatalytic removal of organic pollutants and Cr(VI) ions

Hierarchical dandelion-flower-like cobalt-phosphide modified CdS/reduced graphene oxide-MoS2 nanocomposites as a noble-metal-free catalyst for efficient hydrogen evolution from water

Synthesis, characterization, optical and magnetic properties of pure and Mn, Fe and Zn doped NiO nanoparticles

Benefiting from the maximum atom-utilization efficiency and distinct structural features, single-atom catalysts open a new avenue for the design of more functional catalysts, whereas their bioapplications are still in their infancy Due to the advantages, platinum single atoms supported by cadmium sulfide nanorods (Pt SAs-CdS) are synthesized to build an ultrasensitive photoelectrochemical (PEC) biosensing platform With the decoration of Pt SAs, the PEC signal of CdS is significantly boosted Furthermore, theory calculations indicate the positively charged Pt SAs could change the charge distribution and increase the excited carrier density of CdS Meanwhile, it also suggests that Cu2+ can severely hinder the photoexcitation and electron-hole separation of CdS As a proof of concept, prostate-specific antigen is chosen as the target analyte to demonstrate the superiority of the Pt SAs-CdS-based PEC sensing system As a result, the PEC biosensor based on Pt SAs-CdS exhibits outstanding detection sensitivity and promising applicability

Single-Atom-Based Heterojunction Coupling with Ion-Exchange Reaction for Sensitive Photoelectrochemical Immunoassay.

Room temperature ferromagnetism and white light emissive CdS:Cr nanoparticles synthesized by chemical co-precipitation method

A fully convolutional neural network was used to measure the evolution of the volume fraction of two different Kirkendall pores during the homogenization of Ti coated Ni wires. Traditional methods like Otsus thresholding and the largest connected component analysis were used to obtain the masks for training the segmentation model. Once trained, the model was used to semantically segment the two types of pores at different stages in their evolution. Masks of the pores predicted by the network were then used to measure the volume fraction of porosity at 0 mins, 240 mins, and 480 mins of homogenization. The model predicted an increase in porosity for one type of pore and a decrease in porosity for another type of pore due to pore sintering, and it achieved an F1 Score of 0.95.

Semantic Segmentation of Porosity in 4D Spatio-Temporal X-ray μCT of Titanium Coated Ni wires using Deep Learning

Convolutional neural networks (CNNs) have demonstrated remarkable success in vision-related tasks. However, their susceptibility to failing when inputs deviate from the training distribution is well-documented. Recent studies suggest that CNNs exhibit a bias toward texture instead of object shape in image classification tasks, and that background information may affect predictions. This paper investigates the ability of CNNs to adapt to different color distributions in an image while maintaining context and background. The results of our experiments on modified MNIST and FashionMNIST data demonstrate that changes in color can substantially affect classification accuracy. The paper explores the effects of various regularization techniques on generalization error across datasets and proposes a minor architectural modification utilizing the dropout regularization in a novel way that enhances model reliance on color-invariant intensity-based features for improved classification accuracy. Overall, this work contributes to ongoing efforts to understand the limitations and challenges of CNNs in image classification tasks and offers potential solutions to enhance their performance.

Pradyumna Elavarthi

Papers

Room temperature ferromagnetism and white light emissive CdS:Cr nanoparticles synthesized by chemical co-precipitation method

Semantic Segmentation of Porosity in 4D Spatio-Temporal X-ray μCT of Titanium Coated Ni wires using Deep Learning

On the ability of CNNs to extract color invariant intensity based features for image classification