Nanjing University of Science and Technology

About: Nanjing University of Science and Technology is a education organization based out in Nanjing, China. It is known for research contribution in the topics: Control theory & Catalysis. The organization has 31581 authors who have published 36390 publications receiving 525474 citations. The organization is also known as: Nánjīng Lǐgōng Dàxué & Nánlǐgōng.

A Voltage-Boosting Strategy Enabling a Low-Frequency, Flexible Electromagnetic Wave Absorption Device.

Abstract: Nowadays, low-frequency electromagnetic interference (<2.0 GHz) remains a key core issue that plagues the effective attenuation performance of conventional absorption devices prepared via the component-morphology method (Strategy I). According to theoretical calculations, one fundamental solution is to develop a material that possesses a high e' but lower e″. Thus, it is attempted to control the dielectric values via applying an external electrical field, which inducts changes in the macrostructure toward a performance improvement (Strategy II). A sandwich-structured flexible electronic absorption device is designed using a carbon film electrode to conduct an external current. Simultaneously, an absorption layer that is highly responsive to an external voltage is selected via Strategy I. Relying on the synergistic effects from Strategies I and II, this device demonstrates an absorption value of more than 85% at 1.5-2.0 GHz with an applied voltage of 16 V while reducing the thickness to ≈5 mm. In addition, the device also shows a good absorption property at 25-150 °C. The method of utilizing an external voltage to break the intrinsic dielectric feature by modifying a traditional electronic absorption device is demonstrated for the first time and has great significance in solving the low-frequency electromagnetic interference issue.

Back stress strengthening and strain hardening in gradient structure

Abstract: We report significant back stress strengthening and strain hardening in gradient structured (GS) interstitial-free (IF) steel. Back stress is long-range stress caused by the pileup of geometrically necessary dislocations (GNDs). A simple equation and a procedure are developed to calculate back stress basing on its formation physics from the tensile unloading–reloading hysteresis loop. The gradient structure has mechanical incompatibility due to its grain size gradient. This induces strain gradient, which needs to be accommodated by GNDs. Back stress not only raises the yield strength but also significantly enhances strain hardening to increase the ductility.Impact Statement: Gradient structure leads to high back stress hardening to increase strength and ductility. A physically sound equation is derived to calculate the back stress from an unloading/reloading hysteresis loop.

Stabilizing Cesium Lead Halide Perovskite Lattice through Mn(II) Substitution for Air-Stable Light-Emitting Diodes.

Abstract: All-inorganic cesium lead halide perovskite (CsPbX3, X = Cl, Br, and I) quantum dots (QDs), possessing high photoluminescence quantum yields and tunable color output, have recently been endowed great promise for high-performance solar cells and light-emitting diodes (LEDs). Although moisture stability has been greatly improved through separating QDs with a SiO2 shell, the practical applications of CsPbX3 QDs are severely restricted by their poor thermal stability, which is associated with the intrinsically low formation energies of perovskite lattices. In this regard, enhancing the formation energies of perovskite lattices of CsPbX3 QDs holds great promise in getting to the root of their poor thermal stability, which hitherto remains untouched. Herein, we demonstrate an effective strategy through Mn2+ substitution to fundamentally stabilize perovskite lattices of CsPbX3 QDs even at high temperatures up to 200 °C under ambient air conditions. We employ first-principle calculations to confirm that the signi...

MASS: Masked Sequence to Sequence Pre-training for Language Generation

Abstract: Pre-training and fine-tuning, e.g., BERT, have achieved great success in language understanding by transferring knowledge from rich-resource pre-training task to the low/zero-resource downstream tasks. Inspired by the success of BERT, we propose MAsked Sequence to Sequence pre-training (MASS) for the encoder-decoder based language generation tasks. MASS adopts the encoder-decoder framework to reconstruct a sentence fragment given the remaining part of the sentence: its encoder takes a sentence with randomly masked fragment (several consecutive tokens) as input, and its decoder tries to predict this masked fragment. In this way, MASS can jointly train the encoder and decoder to develop the capability of representation extraction and language modeling. By further fine-tuning on a variety of zero/low-resource language generation tasks, including neural machine translation, text summarization and conversational response generation (3 tasks and totally 8 datasets), MASS achieves significant improvements over the baselines without pre-training or with other pre-training methods. Specially, we achieve the state-of-the-art accuracy (37.5 in terms of BLEU score) on the unsupervised English-French translation, even beating the early attention-based supervised model.