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.

Superior strength and ductility of 316L stainless steel with heterogeneous lamella structure

Abstract: Strength and ductility are two of the most important mechanical properties for a metal, but often trade off with each other. Here, we report a 316L stainless steel with superior combinations of strength and ductility that can be controlled by fine-tuning its heterogeneous lamella structure (HLS). The HLS was produced by 85% cold rolling, which produced lamellar coarse grains sandwiched between mixtures of nano-grains and nano-twins. The HLS was fine-tuned by annealing at 750 °C for 5–25 min, which resulted in varying volume fractions of nano-grains, nano-twins, lamellar coarse grains, and recrystallized grains. During tensile testing, large amount of geometrically necessary dislocations were generated near the heterostructure interfaces to coordinate the deformation between soft domains and hard domains, which results in high back stress to achieve superior combination of strength and ductility. An optimal high yield strength of ~ 1 GPa with an elongation-to-failure of ~ 20% was obtained for an optimized HLS sample. Furthermore, the processing technique employed here is conducive to large-scale industrial production at low cost.

Few-Layered Tin Sulfide Nanosheets Supported on Reduced Graphene Oxide as a High-Performance Anode for Potassium-Ion Batteries.

Abstract: Anodes involving conversion and alloying reaction mechanisms are attractive for potassium-ion batteries (PIBs) due to their high theoretical capacities. However, serious volume change and metal aggregation upon potassiation/depotassiation usually cause poor electrochemical performance. Herein, few-layered SnS2 nanosheets supported on reduced graphene oxide (SnS2 @rGO) are fabricated and investigated as anode material for PIBs, showing high specific capacity (448 mAh g-1 at 0.05 A g-1 ), high rate capability (247 mAh g-1 at 1 A g-1 ), and improved cycle performance (73% capacity retention after 300 cycles). In this composite electrode, SnS2 nanosheets undergo sequential conversion (SnS2 to Sn) and alloying (Sn to K4 Sn23 , KSn) reactions during potassiation/depotassiation, giving rise to a high specific capacity. Meanwhile, the hybrid ultrathin nanosheets enable fast K storage kinetics and excellent structure integrity because of fast electron/ionic transportation, surface capacitive-dominated charge storage mechanism, and effective accommodation for volume variation. This work demonstrates that K storage performance of alloy and conversion-based anodes can be remarkably promoted by subtle structure engineering.

Approximate Orthogonal Sparse Embedding for Dimensionality Reduction

Abstract: Locally linear embedding (LLE) is one of the most well-known manifold learning methods. As the representative linear extension of LLE, orthogonal neighborhood preserving projection (ONPP) has attracted widespread attention in the field of dimensionality reduction. In this paper, a unified sparse learning framework is proposed by introducing the sparsity or $L_{1}$ -norm learning, which further extends the LLE-based methods to sparse cases. Theoretical connections between the ONPP and the proposed sparse linear embedding are discovered. The optimal sparse embeddings derived from the proposed framework can be computed by iterating the modified elastic net and singular value decomposition. We also show that the proposed model can be viewed as a general model for sparse linear and nonlinear (kernel) subspace learning. Based on this general model, sparse kernel embedding is also proposed for nonlinear sparse feature extraction. Extensive experiments on five databases demonstrate that the proposed sparse learning framework performs better than the existing subspace learning algorithm, particularly in the cases of small sample sizes.

Computational Studies on Polynitrohexaazaadmantanes as Potential High Energy Density Materials

Abstract: Polynitrohexaazaadamantanes (PNHAAs) have been the subject of much recent research because of their potential as high energy density materials (HEDMs). The B3LYP/6-31G* method was employed to evaluate the heats of formation (HOFs) for PNHAAs by designing isodesmic reactions. The HOFs are found to be correlative with the number (n) and the space orientations of nitro groups. Detonation velocities (D) and detonation pressures (P) were estimated for PNHAAs by using the well-known Kamlet−Jacobs equations, based on the theoretical densities (ρ) and HOFs. It is found that D and P increase as n ranges from 1 to 6, and PNHAAs with 4−6 nitro groups meet the criteria of an HEDM. When n is over 6, ρ of PNHAAs slightly increases; however, the chemical energy of detonation (Q) decreases so greatly that both D and P decrease. The calculations on bond dissociation energies suggest that the N−N bond be the trigger bond during the pyrolysis initiation process of each PNHAA, and with increasing n, N−N bond dissociation ene...