All Outputs (79)

Mechanically tunable metasurface with large gamut of color: Lateral hybrid system (2022)
Journal Article

Hybrid metasurfaces are made of metals and dielectrics in which dielectrics (metals) are sandwiched between metals (dielectrics) to control the reflection and transmission of light. The existing designs have low sensitivity, little color coverage, an... Read More about Mechanically tunable metasurface with large gamut of color: Lateral hybrid system.

In-Materio Extreme Learning Machines (2022)
Book Chapter

Nanomaterial networks have been presented as a building block for unconventional in-Materio processors. Evolution in-Materio (EiM) has previously presented a way to congure and exploit physical materials for computation, but their ability to scale as... Read More about In-Materio Extreme Learning Machines.

Towards Intelligently Designed Evolvable Processors (2022)
Journal Article

Evolution-in-Materio is a computational paradigm in which an algorithm reconfigures a material’s properties to achieve a specific computational function. This paper addresses the question of how successful and well performing Evolution-in-Materio pro... Read More about Towards Intelligently Designed Evolvable Processors.

Air-Gapped Current Transformer simulation and accuracy assessment (2022)
Presentation / Conference Contribution

The implementation of smart grids makes it necessary to have reliable and efficient measuring devices. Current sensors used for metering purposes in the grid must be accurate enough to overcome new challenges that power networks face such as injectio... Read More about Air-Gapped Current Transformer simulation and accuracy assessment.

Enhanced Methods for Evolution in-Materio Processors (2022)
Presentation / Conference Contribution

Evolution-in-Materio (EiM) is an unconventional computing paradigm, which uses an Evolutionary Algorithm (EA) to configure a material's parameters so that it can perform a computational task. While EiM processors show promise, slow manufacturing and... Read More about Enhanced Methods for Evolution in-Materio Processors.

Electrical behaviour and evolutionary computation in thin films of bovine brain microtubules (2021)
Journal Article

We report on the electrical behaviour of thin films of bovine brain microtubules (MTs). For samples in both their dried and hydrated states, the measured currents reveal a power law dependence on the applied DC voltage. We attribute this to the injec... Read More about Electrical behaviour and evolutionary computation in thin films of bovine brain microtubules.

Fabrication and characterization of cellulose acetate-based nanofibers and nanofilms for H2S gas sensing application (2021)
Journal Article

Electrospun nanofibers and solution-casting nanofilms were produced from an environmentally friendly cellulose acetate (CA) blended with glycerol (as an ionic liquid (IL)), mixed with polypyrrole (PPy, a conducting polymer) and doped with tungsten ox... Read More about Fabrication and characterization of cellulose acetate-based nanofibers and nanofilms for H2S gas sensing application.

Optical properties and carrier dynamics in Co-doped ZnO nanorods (2020)
Journal Article

The controlled modification of the electronic properties of ZnO nanorods via transition metal doping is reported. A series of ZnO nanorods were synthesized by chemical bath growth with varying Co content from 0 to 20 atomic% in the growth solution. O... Read More about Optical properties and carrier dynamics in Co-doped ZnO nanorods.

Cobalt-Doped ZnO Nanorods Coated with Nanoscale Metal-Organic Framework Shells for Water-Splitting Photoanodes (2020)
Journal Article

Developing highly efficient and stable photoelectrochemical (PEC) water splitting electrodes via inexpensive, liquid phase processing is one of the key challenges for the conversion of solar energy into hydrogen for sustainable energy production. ZnO... Read More about Cobalt-Doped ZnO Nanorods Coated with Nanoscale Metal-Organic Framework Shells for Water-Splitting Photoanodes.

Ultrasensitive and low temperature gas sensor based on electrospun organic-inorganic nanofibers (2020)
Journal Article

Organic-inorganic hybrid material is one of the most promising materials for high performance gas sensors due to its improved properties like high sensitivity, selectivity, fast response time, flexibility and low power consumption. This work presents... Read More about Ultrasensitive and low temperature gas sensor based on electrospun organic-inorganic nanofibers.

Controlling the growth of single crystal ZnO nanowires by tuning the atomic layer deposition parameters of the ZnO seed layer (2019)
Journal Article

Semiconducting nanowires (NWs) offer exciting prospects for a wide range of technological applications. The translation of NW science into technology requires reliable high quality large volume production. This study provides an in-depth investigatio... Read More about Controlling the growth of single crystal ZnO nanowires by tuning the atomic layer deposition parameters of the ZnO seed layer.

Confidence Measures for Carbon-Nanotube / Liquid Crystals Classifiers (2018)
Presentation / Conference Contribution

This paper focuses on a performance analysis of single-walled-carbon-nanotube / liquid crystal classifiers produced by evolution in materio. A new confidence measure is proposed in this paper. It is different from statistical tools commonly used to e... Read More about Confidence Measures for Carbon-Nanotube / Liquid Crystals Classifiers.

A simplified model to estimate thermal resistance between carbon nanotube and sample in scanning thermal microscopy (2017)
Journal Article

Scanning thermal microscopy (SThM) is an attractive technique for nanoscale thermal measurements. Multiwalled carbon nanotubes (MWCNT) can be used to enhance a SThM probe in order to drastically increase spatial resolution while keeping required ther... Read More about A simplified model to estimate thermal resistance between carbon nanotube and sample in scanning thermal microscopy.

Field imaging near to the surface of terahertz reflective optics using a vector network analyzer (2017)
Journal Article

A vector network analyzer-based quasi-optical measurement system that is suitable for mapping electric field intensity and phase near to the surface of terahertz reflective optics is presented. The system uses a fixed five parabolic mirror and transm... Read More about Field imaging near to the surface of terahertz reflective optics using a vector network analyzer.

Organic Floating-Gate Memory Structures (2017)
Book Chapter

Model for large-area monolayer coverage of polystyrene nanospheres by spin coating (2017)
Journal Article

Nanosphere lithography, an inexpensive and high throughput technique capable of producing nanostructure (below 100 nm feature size) arrays, relies on the formation of a monolayer of self-assembled nanospheres, followed by custom-etching to produce na... Read More about Model for large-area monolayer coverage of polystyrene nanospheres by spin coating.

Investigation of non-equilibrium electron-hole plasma in nanowires by THz spectroscopy (2016)
Journal Article

Single-Walled Carbon-Nanotubes-Based Organic Memory Structures (2016)
Journal Article

The electrical behaviour of organic memory structures, based on single-walled carbon-nanotubes (SWCNTs), metal–insulator–semiconductor (MIS) and thin film transistor (TFT) structures, using poly(methyl methacrylate) (PMMA) as the gate dielectric, are... Read More about Single-Walled Carbon-Nanotubes-Based Organic Memory Structures.

Evolution of Electronic Circuits using Carbon Nanotube Composites (2016)
Journal Article

Evolution-in-materio concerns the computer controlled manipulation of material systems using external stimuli to train or evolve the material to perform a useful function. In this paper we demonstrate the evolution of a disordered composite material,... Read More about Evolution of Electronic Circuits using Carbon Nanotube Composites.

Training a Carbon-Nanotube/Liquid Crystal Data Classifier Using Evolutionary Algorithms (2016)
Presentation / Conference Contribution

Evolution-in-Materio uses evolutionary algorithms (EA) to exploit the physical properties of unconfigured, physically rich materials, in effect transforming them into information processors. The potential of this technique for machine learning proble... Read More about Training a Carbon-Nanotube/Liquid Crystal Data Classifier Using Evolutionary Algorithms.