Results 81 to 90 of about 33,969 (261)
Single‐Chromophore Homojunction Organic Solar Cells: A Path to Simplicity and Efficiency
This perspective discusses how the intrinsic optoelectronic properties of organic semiconductors, their molecular packing in the solid‐state, and internal energetic gradients within a device can enable free‐charge carrier generation in homojunction organic solar cells.
Shaun McAnally +2 more
wiley +1 more source
This review highlights the role of self‐assembled monolayers (SAMs) in perovskite solar cells, covering molecular engineering, multifunctional interface regulation, machine learning (ML) accelerated discovery, advanced device architectures, and pathways toward scalable fabrication and commercialization for high‐efficiency and stable single‐junction and
Asmat Ullah, Ying Luo, Stefaan De Wolf
wiley +1 more source
Quantum Computing Meets Deep Learning: A QCNN Model for Accurate and Efficient Image Classification
In deep learning, Convolutional Neural Networks (CNNs) serve as fundamental models, leveraging the correlational structure of data for tasks such as image classification and processing.
Sunil Prajapat +4 more
doaj +1 more source
Optimizing Quantum Classification Algorithms on Classical Benchmark Datasets. [PDF]
John M +4 more
europepmc +1 more source
Transparent Perovskite Light‐Emitting Diodes with Conductive Oxide Top Electrodes
Transparent perovskite light‐emitting diodes (TrPeLEDs) enable simultaneous display and transparency, expanding application possibilities. Using a metal oxide buffer layer and pulsed laser deposition, TrPeLEDs with diverse compositions and architectures are demonstrated.
Michele Forzatti +11 more
wiley +1 more source
A hybrid QSVM-QNN approach for breast cancer diagnosis using quantum feature maps [PDF]
Quantum assisted machine learning (QML) have the potential to outperform the classical Machine Learning models by utilizing the quantum feature maps embeddings and variational circuits.
Vikesh Yadav +2 more
doaj +2 more sources
Quantum Machine Learning for Malware Classification
In a context of malicious software detection, machine learning (ML) is widely used to generalize to new malware. However, it has been demonstrated that ML models can be fooled or may have generalization problems on malware that has never been seen. We investigate the possible benefits of quantum algorithms for classification tasks.
Grégoire Barrué, Tony Quertier
openaire +2 more sources
Molecular doping of conjugated polymers is fundamentally constrained by thermodynamic phase behavior. This Perspective reframes doping efficiency and stability in terms of miscibility limits, binodals, and solvus boundaries, highlighting the role of effective interaction parameters and charge transfer.
Somayeh Kashani +10 more
wiley +1 more source
Controlling the protein corona formation onto carbon nanomaterials (CNMs) enhances their functionalities as platforms for cancer theranostics. Here, we reviewed the effects of the intrinsic and acquired properties of CNMs on protein corona formation, the consequent biological and toxicological outcomes, and the strategies to reshape corona formation ...
Yajuan Zou +5 more
wiley +1 more source
Quantum neural networks facilitating quantum state classification
The classification of quantum states into distinct classes poses a significant challenge. In this study, we address this problem using quantum neural networks in combination with a problem-inspired circuit and customised as well as predefined ansätz. To facilitate the resource-efficient quantum state classification, we construct the dataset of quantum ...
Diksha Sharma +3 more
openaire +2 more sources

