The optical spectra of DMAC based molecules for organic light‐emitting diodes: Hybrid‐exchange density functional theory study

Organic light-emitting diodes (OLED) have considerable advantages over the conventional counterpart. Molecular design by simulations is important for the discovery of new material candidate to improve the performance of OLED. Recently, thermally assisted delayed fluorescence OLED based on DMAC (9,9-dimethyl-9,10-dihydroacridine)-related molecules have been found to have superior performance. In this work, a series of first-principles calculations are performed on DMAC-DPS (diphenylsulfone, emission of blue-color light), DMAC-BP (benzophenone, green), DMAC-DCPP (dicyclohexylphosphonium, red), and the newly designed DMAC-BF (enaminone difluoroboron complexes, red) molecules, based on time-dependent density-functional theory, the hybrid-exchange density functional, and the long-range corrected hybrid-exchange density functional. By varying the percentage of Hartree–Fock (HF) exchange in the hybrid-exchange functional, the emission spectra can be over 97% fitted to the experimental results. We found that the fitted proportion of HF will increase as the wavelengths of the molecules decrease (30% for DPS, 20% for BP, and 10% for DCPP). By contrast, the long-range corrected hybrid-exchange density functional can lead to a good estimate on the absorption spectra. In addition, we have also applied our fitting computational procedure to the newly designed molecule. The molecular orbitals involved in the related excited states have also been investigated for these molecules, which show a common charge-transfer characteristic between the acceptor part (DPS/BP/DCPP/BF) and the donor (DMAC)

Density-functional-theory simulations of the water and ice adhesion on silicene quantum dots

The absorption of water and ice on silicon is important to understand for many applications and safety concerns for electronic devices as most of them are fabricated using silicon. Meanwhile, recently silicene nanostructures have attracted much attention due to their potential applications in electronic devices such as gas or humidity sensors. However, for the moment, the theoretical study of the interaction between water molecules and silicene nanostructures is still rare although there is already theoretical work on the effect of water molecules on the silicene periodic structure. The specific conditions such as the finite size effect, the edge saturation of the silicene nanostructure, and the distance between the water/ice and the silicene at the initial onset of the contact have not been carefully considered before. Here we have modelled the absorption of a water molecule and a square ice on the silicene nanodot by using hybrid-exchange density-functional theory, complemented by the Van der Waals forces correction. Three different sizes of silicene nanodots have been chosen for simulations, namely 3×3, 4×4, and 5×5, with and without the hydrogen saturation on the edge. Our calculations suggest that the silicene nanodots chosen here are both hydrophilic and ice-philic. The water molecule and the square ice have tilted angles towards the silicene nanodot plane at ~ 70º and ~ 45º, respectively, which could be owing to the zig–zag structure on silicene. The absorption energies are size dependent for unsaturated silicene nanodots, whereas almost size independent for the hydrogen saturated cases. Our work on the single water molecule absorption energy on silicene nanodots is qualitatively in agreement with the previous theoretical and experimental work. However, the ice structure on silicene is yet to be validated by the relevant experiments. Our calculation results not only further complement the current paucity of water-to-silicene-nanostructure contact mechanisms, but also lead to the first study of square-ice contact mechanisms for silicene. Our findings presented here could be useful for the future design of semiconducting devices based on silicene nanostructures, especially in the humid and low-temperature environments

Simulating the friction between atomic layers by using a two-q model: Analysis of the relative motion and coherence

Studying friction between atomic layers is not only of great interest for the fundamental aspect of the tribology but also important for many applications such as the layer adhesion in wearable technologies and energy saving. The previous theoretical study has used the modified Prandtl-Tomlinson model to describe the motion of the tip above a two-dimensional atomic layer in an atomic force microscopy experiment. Here the degree of freedom for the substrate has been further explicitly included in the simulation, which is significant because the coherence between the sensing and the substrate layers can be explicitly addressed by computing their relative motion. For both layers, graphene has been chosen as an example for the simulations. Based on the simulations reported here, which agree with the previous relevant theoretical and atomic-force-microscopy experimental results, the motions between the sensing sheet and the substrate can be clearly distinguished. The dependence of motion and force on the parameters for the mechanical properties of the individual layers and the interaction potential between the layers has been carefully studied. For the relatively large values of the parameters for the mechanical properties, the relative motions between the sensing sheet and the substrate show that there would be coherence between the layers, which is beneficial for the adhesion between them. However, many other parameter spaces can be studied further in the future. Similar to the simulations of the motions of the atomic layers, the computed force of the atomic-force-microscopy tip can also indicate the stability of the layers. The theoretical work reported can be used to identify explicitly the relative motions between the sensing sheet and the substrate, providing a substantial improvement for the understanding of the friction between atomic layers. Moreover, in principles, the modeling methodology proposed can be generalized to describe any number of layers in the thin-film devices, by adding a q-parameter for each layer

The adhesive energies between P3HT and PVP for organic electronic devices: Hybrid-exchange density-functional-theory studies

Studying the building blocks for organic electronics – molecules – is important for achieving a great performance for organic electronic devices. Poly(3-hexylthiophene) (P3HT) and povidone (PVP) are common molecules chosen for the semiconducting and dielectric layers of organic electronic devices, respectively. Here we have applied the hybrid-exchange density-functional theory, taking into account empirical dispersion forces and basis set superposition erros, to study the adhesive energies and optimal geometries when integrating the two types of molecules. To ease the analysis of the molecular structures, we have simplified the polymer chain structure to the monomer, dimer, and trimer for the P3HT and PVP. By using B3LYP and BLYP functionals in combination with dispersion forces, we have found the optimal inter-molecular vertical distances between P3HT and PVP are approximately 3.6 Å, 6 Å and 5 Å, for monomer, dimer, and trimer, respectively, with the lowest adsorption energy of ~-0.35, -0.15 and -0.45 eV. However, the sliding effect for the molecular combination is relatively small. These computational results can be potentially compared with the relevant experiments on the molecular crystal structure. The molecular orbitals of the P3HT and PVP molecules show that the charge density is mainly on the five-member rings rather than the polymer chains, which further supports our finite-chain approximation. Our calculations, especially the potential curves, could be useful for the optimal design of molecular structures for organic electronic devices

High Dielectric Constants in BaTiO3 Due to Phonon Mode Softening Induced by Lattice Strains: First Principles Calculations

High-dielectric-constant materials attract much attention due to their broad applications in modern electronics. Barium titanate (BTO) is an established material possessing an ultrahigh dielectric constant; however, a complete understanding of the responsible underlying physical mechanism remains elusive. Here a set of density-functional-theory calculations for the static dielectric tensors of barium titanate under strain has been performed. The dielectric constant increases to ≈7300 under strain. The analysis of the computed vibrational modes shows that transverse vibrational mode softening (the appearance of low-frequency modes) is responsible for this significant increase as driven by the relationship between lattice contribution for the static dielectric constant (k) and vibrational frequency (ω), i.e., urn:x-wiley:27511200:media:apxr202300001:apxr202300001-math-0001. The relevant vibrational mode indicates a large counter-displacement of Ti ions and O anions, which greatly enhances electrical dipoles to screen the electric field. The calculations not only interpreted experimental data on the high dielectric constants of BTO, where the lattice deformation due to the strains from the grain nanostructure plays an important role, but also pointed to exploring high-throughput calculations to facilitate the discovery of the advanced dielectric materials. Moreover, the calculations can prove useful for doped BTO, for which local strains fields can be achieved using defect engineering

Bi-Real Net: Enhancing the Performance of 1-bit CNNs With Improved Representational Capability and Advanced Training Algorithm

In this work, we study the 1-bit convolutional neural networks (CNNs), of which both the weights and activations are binary. While being efficient, the classification accuracy of the current 1-bit CNNs is much worse compared to their counterpart real-valued CNN models on the large-scale dataset, like ImageNet. To minimize the performance gap between the 1-bit and real-valued CNN models, we propose a novel model, dubbed Bi-Real net, which connects the real activations (after the 1-bit convolution and/or BatchNorm layer, before the sign function) to activations of the consecutive block, through an identity shortcut. Consequently, compared to the standard 1-bit CNN, the representational capability of the Bi-Real net is significantly enhanced and the additional cost on computation is negligible. Moreover, we develop a specific training algorithm including three technical novelties for 1- bit CNNs. Firstly, we derive a tight approximation to the derivative of the non-differentiable sign function with respect to activation. Secondly, we propose a magnitude-aware gradient with respect to the weight for updating the weight parameters. Thirdly, we pre-train the real-valued CNN model with a clip function, rather than the ReLU function, to better initialize the Bi-Real net. Experiments on ImageNet show that the Bi-Real net with the proposed training algorithm achieves 56.4% and 62.2% top-1 accuracy with 18 layers and 34 layers, respectively. Compared to the state-of-the-arts (e.g., XNOR Net), Bi-Real net achieves up to 10% higher top-1 accuracy with more memory saving and lower computational cost. Keywords: binary neural network, 1-bit CNNs, 1-layer-per-blockComment: Accepted to European Conference on Computer Vision (ECCV) 2018. Code is available on: https://github.com/liuzechun/Bi-Real-ne

Prolonged mixed phase induced by high pressure in MnRuP

Hexagonally structured MnRuP was studied under high pressure up to 35 GPa from 5 to 300 K using synchrotron X-ray diffraction. We observed that a partial phase transition from hexagonal to orthorhombic symmetry started at 11 GPa. The new and denser orthorhombic phase coexisted with its parent phase for an unusually long pressure range, {\Delta}P ~ 50 GPa. We attribute this structural transformation to a magnetic origin, where a decisive criterion for the boundary of the mixed phase lays in the different distances between the Mn-Mn atoms. In addition, our theoretical study shows that the orthorhombic phase of MnRuP remains steady even at very high pressures up to ~ 250 GPa, when it should transform to a new tetragonal phase.Comment: 15 pages, 5 figures, supplementary materia

A novel and anti-agglomerating Ni@yolk–ZrO₂ structure with sub-10 nm Ni core for high performance steam reforming of methane

Steam reforming of methane is a versatile technology for hydrogen production in oil refinery and fuel cell applications. Using natural gas is a promising method to produce rich-hydrogen gas. Ni@yolk–ZrO₂ catalyst is used to study steam reforming of methane under various GHSVs, steam-to-carbon (S/C) ratio, and its recyclability. The catalyst was characterized using a combination of XRD, TEM, AAS, TPR, TPH, TGA, BET, XPS, and Raman techniques. The catalyst is evaluated on time stream and identify its anti-agglomeration property and coking mechanism. From the characterization of TEM and XPS establish the information of Ni particles mobility in the catalyst, which active metal particle size was controlled under the yolk–shell structure framework. Furthermore, the results from TGA, TPH, and Raman analysis of the used Ni@yolk–ZrO₂ catalyst showed the characteristic of inhibiting formation of highly ordered carbon structure

Elevated BCRP/ABCG2 Expression Confers Acquired Resistance to Gefitinib in Wild-Type EGFR-Expressing Cells

The sensitivity of non-small cell lung cancer (NSCLC) patients to EGFR tyrosine kinase inhibitors (TKIs) is strongly associated with activating EGFR mutations. Although not as sensitive as patients harboring these mutations, some patients with wild-type EGFR (wtEGFR) remain responsive to EGFR TKIs, suggesting that the existence of unexplored mechanisms renders most of wtEGFR-expressing cancer cells insensitive.Here, we show that acquired resistance of wtEGFR-expressing cancer cells to an EGFR TKI, gefitinib, is associated with elevated expression of breast cancer resistance protein (BCRP/ABCG2), which in turn leads to gefitinib efflux from cells. In addition, BCRP/ABCG2 expression correlates with poor response to gefitinib in both cancer cell lines and lung cancer patients with wtEGFR. Co-treatment with BCRP/ABCG2 inhibitors enhanced the anti-tumor activity of gefitinib.Thus, BCRP/ABCG2 expression may be a predictor for poor efficacy of gefitinib treatment, and targeting BCRP/ABCG2 may broaden the use of gefitinib in patients with wtEGFR