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: Catalysis & Computer science. The organization has 31581 authors who have published 36390 publications receiving 525474 citations. The organization is also known as: Nánjīng Lǐgōng Dàxué & Nánlǐgōng.

Pd nanoparticles supported on low-defect graphene sheets: for use as high-performance electrocatalysts for formic acid and methanol oxidation

Abstract: Well-dispersed Pd nanoparticles supported on low-defect graphene (LDG) sheets are successfully prepared by a soft chemical method. Our approach can efficiently avoid damaging the graphene framework in the composite because it does not require cumbersome oxidation of graphite in advance and needs no subsequent reduction of the LDG sheets due to the lower oxidation degree. Morphology observations show that the Pd nanoparticles with diameters ranging from 1 to 5 nm are evenly deposited on graphene sheets. Raman spectroscopic analysis results reveals that there is only a very small amount of graphene defects in the hybrid. No matter whether it is for a direct formic acid fuel cell (DFAFC) or direct methanol fuel cell (DMFC), the LDG-supported Pd catalyst has very large electrochemically active surface area (ECSA) values, more than twice as large as that for the reduced graphene oxide, or five times the commercial XC-72 carbon. The forward peak current measurements show similar results. The excellent catalytic performance of LDG/Pd can be attributed to the preserved pristine graphene structure, which not only provides a lot of surface area for the deposition of nanoparticles, but also allows for electrical conductivity and stability in the composite.

Physical unclonable functions

Abstract: A physical unclonable function (PUF) is a device that exploits inherent randomness introduced during manufacturing to give a physical entity a unique ‘fingerprint’ or trust anchor. These devices are .of potential use in a variety of applications from anti-counterfeiting, identification, authentication and key generation to advanced protocols such as oblivious transfer, key exchange, key renovation and virtual proof of reality. Here we review the development of PUFs, including those that exploit optical, circuit time-delay and volatile/non-volatile memory characteristics. We examine the various applications of PUFs, and consider the security issues that they must confront, highlighting known attacks to date and potential countermeasures. We also consider the key areas for future development such as bit-specific reliability, reconfigurability and public key infrastructure. This Review Article examines the development of physical unclonable functions, which exploit inherent randomness to give a physical entity a unique ‘fingerprint’ or trust anchor, considering the various potential applications of these devices and the security issues that they must confront.

Next-generation ammonia pretreatment enhances cellulosic biofuel production

Abstract: A new liquid ammonia pretreatment methodology called Extractive Ammonia (EA) was developed to simultaneously convert native crystalline cellulose Iβ (CI) to a highly digestible cellulose IIII (CIII) allomorph and selectively extract up to ∼45% of the lignin from lignocellulosic biomass with near-quantitative retention of all polysaccharides. EA pretreated corn stover yielded a higher fermentable sugar yield compared to the older Ammonia Fiber Expansion (AFEX) process while using 60% lower enzyme loading. The EA process preserves extracted lignin functionalities, offering the potential to co-produce lignin-derived fuels and chemicals in the biorefinery. The single-stage EA fractionation process achieves high biofuel yields (18.2 kg ethanol per 100 kg untreated corn stover, dry weight basis), comparable to those achieved using ionic liquid pretreatments. The EA process achieves these ethanol yields at industrially-relevant conditions using low enzyme loading (7.5 mg protein per g glucan) and high solids loading (8% glucan, w/v).

A Locality-Constrained and Label Embedding Dictionary Learning Algorithm for Image Classification

Abstract: Locality and label information of training samples play an important role in image classification. However, previous dictionary learning algorithms do not take the locality and label information of atoms into account together in the learning process, and thus their performance is limited. In this paper, a discriminative dictionary learning algorithm, called the locality-constrained and label embedding dictionary learning (LCLE-DL) algorithm, was proposed for image classification. First, the locality information was preserved using the graph Laplacian matrix of the learned dictionary instead of the conventional one derived from the training samples. Then, the label embedding term was constructed using the label information of atoms instead of the classification error term, which contained discriminating information of the learned dictionary. The optimal coding coefficients derived by the locality-based and label-based reconstruction were effective for image classification. Experimental results demonstrated that the LCLE-DL algorithm can achieve better performance than some state-of-the-art algorithms.