Peptide-based molecular electronic devices are promising due to the large diversity and unique electronic properties of biomolecules. These electronic properties can change considerably with peptide structure, allowing diverse design possibilities. In this work, we explore the effect of the side-chain of the peptide on its electronic properties, by using both experimental and computational tools to detect the electronic energy levels of two model peptides. The peptides include 2Ala and 2Trp as well as their 3-mercaptopropionic acid linker which is used to form monolayers on an Au surface. Specifically, we compare experimental ultraviolet photoemission spectroscopy measurements with density functional theory based computational results. By analyzing differences in frontier energy levels and molecular orbitals between peptides in gas-phase and in a monolayer on gold, we find that the electronic properties of the peptide side-chain are maintained during binding of the peptide to the gold substrate. This indicates that the energy barrier for the peptide electron transport can be tuned by the amino acid compositions, which suggests a route for structural design of peptide-based electronic devices.
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http://dx.doi.org/10.1039/c7cp08043c | DOI Listing |
ACS Appl Mater Interfaces
December 2024
School of Mathematics and Physics, University of Science and Technology Beijing, Beijing 100083, China.
Although MoSe-based photodetectors have achieved excellent performance, the ultrafast photoresponse has limited their application as an optoelectronic synapse. In this paper, the enhancement of the rhodamine 6G molecule on the memory time of MoSe is reported. It is found that the memory time of monolayer MoSe can be obviously enhanced after assembly with rhodamine 6G exhibiting synaptic characteristics in comparison to pristine MoSe.
View Article and Find Full Text PDFACS Appl Mater Interfaces
December 2024
School of Materials Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.
Magnetocaloric high-entropy alloys (HEAs) have recently garnered significant interest owing to their potential applications in magnetic refrigeration, offering a wide working temperature range and large refrigerant capacity. In this study, we thoroughly investigated the structural, magnetic, and magnetocaloric properties of equiatomic GdDyHoErTm HEAs. The as-cast alloy exhibits a single hexagonal phase, a randomly distributed grain orientation, and complex magnetism.
View Article and Find Full Text PDF<i>Ormocarpum trichocarpum</i> (Taub.) Engl. is a shrub or small tree harvested from the wild as a source of food, traditional medicines and wood.
View Article and Find Full Text PDFSci Rep
December 2024
Department of Engineering, Norfolk State University, Norfolk, USA.
We report a controlled deposition process using atmospheric plasma to fabricate silver nanoparticle (AgNP) structures on polydimethylsiloxane (PDMS) substrates, essential for stretchable electronic circuits in wearable devices. This technique ensures precise printing of conductive structures using nanoparticles as precursors, while the relationship between crystallinity and plasma treatment is established through X-ray diffraction (XRD) analysis. The XRD studies provide insights into the effects of plasma parameters on the structural integrity and adhesion of AgNP patterns, enhancing our understanding of substrate stretchability and bendability.
View Article and Find Full Text PDFNat Comput Sci
December 2024
Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
Machine learning plays an important role in quantum chemistry, providing fast-to-evaluate predictive models for various properties of molecules; however, most existing machine learning models for molecular electronic properties use density functional theory (DFT) databases as ground truth in training, and their prediction accuracy cannot surpass that of DFT. In this work we developed a unified machine learning method for electronic structures of organic molecules using the gold-standard CCSD(T) calculations as training data. Tested on hydrocarbon molecules, our model outperforms DFT with several widely used hybrid and double-hybrid functionals in terms of both computational cost and prediction accuracy of various quantum chemical properties.
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