OAJ Materials and Devices

Template valid for M (methods) and O (Opinion) articles.

2026-04-12T18:27:33+00:00

Template to be used when uploading the revised version of your manuscript. Valid for M (methods) and O (Opinion) articles.

The Nuclear Quantum Gravity + Superconducting Field Theory (ToE)

2026-01-20T16:01:38+00:00

A Theory of Everything (ToE) is any model of physics that explains and connects all fundamental interactions (strong force, electromagnetism, weak force, and gravity) into a single force. Here, we present a framework that integrates chemistry and astrophysics to unify these forces by explaining gravity, dark matter, and dark energy using fundamental physics, without requiring additional dimensions or external forces.

The first part unifies the strong nuclear force with the gravitational force in a mathematical way; the strong nuclear force can deform the quantum vacuum.

The second part unifies the strong nuclear force with the quantum vacuum in a hypothetical structure; the quantum vacuum is treated as a system with properties related to the different types of particles' motion.

Geometrical Dependent Local Filed Enhancement Factor and Optical Induced Bistability of ZnO@Ag Core-Shell Nanocomposite

2026-01-07T05:29:34+00:00

In this study, the author investigated the local field enhancement factor and optically induced bistability (OIB) in spherical and cylindrical ZnO@Ag core–shell nanostructures embedded in a linear host matrix, considering the effects of the interfacial layer as well as the shape and size of the inclusions. The author umerically analyzed the influence of the interfacial layer on the local field enhancement factor and on the cubic equation governing the optically induced bistability of the composite material. The interfacial layer factor was taken as positive, zero, or negative, representing dielectric-like, no-interfacial, and metal-like interfacial properties, respectively. For spherical inclusions, the local field enhancement factor decreases for larger positive and negative values of the interfacial layer factor, whereas for cylindrical inclusions, it decreases as the interfacial factor increases. Results show that the interfacial properties, along with the shape and size of the composite, can significantly affect both the optically induced bistable behavior and the local field enhancement. This optimized arrangement and unique bistable behavior in metal composites with small dielectric cores may be promising for various potential applications like optoelectronics and plasmonic.

Template valid for Review articles.

2026-04-12T18:34:55+00:00

Template to be used when uploading the revised version of your manuscript. Valid for R (Review) articles.

Template regular articles

2026-04-12T18:03:35+00:00

Template to be used when uploading revised version of your article.

The Generative Inverse Design of High-Performance Porous Carbons for CO2 Capture

2025-12-19T14:25:27+00:00

The discovery of novel materials with tailored properties is essential for technological progress, yet traditional trial-and-error approaches remain slow and resource-intensive. Inverse design offers a transformative paradigm by identifying structures that meet predefined performance targets. In this work, we present a deep generative framework for the inverse design of porous carbons optimized for CO2 capture. Using a database of over 20,000 virtual carbon structures, we trained a 3D convolutional Generative Adversarial Network (GAN) capable of learning a compact and continuous representation of the structure-property landscape. A surrogate predictive model based on gradient boosting enables efficient latent-space optimization, guiding the generator toward high-performance morphologies. The resulting AI-designed material, CG-005, features layered graphene-like sheets forming uniform slit-micropores centered at 0.6 nm. Molecular simulations confirm a CO? adsorption capacity of 6.2 mmol/g at 298 K and 1 bar, outperforming benchmark sorbents such as Zeolite 13X and MOF-177. This study demonstrates the power of deep generative models in accelerating the discovery of next-generation, high-performance carbon sorbents for environmental and energy applications.