A dual-functional intelligent logic detector based on new Ln-MOFs: first visual logical probe for the two-dimensional monitoring of pyrethroid biomarkers

2020 ◽  
Vol 8 (9) ◽  
pp. 3023-3028 ◽  
Author(s):  
Yu Zhang ◽  
Xiao Lian ◽  
Bing Yan
Keyword(s):  
Artificial Intelligence ◽  
Two Dimensional ◽  
Dual Functional ◽  
Logic Systems

Nowadays, artificial intelligence (AI) is flourishing in various fields, but the application of complex intelligent logic systems to the two-dimensional monitoring of biomarkers is quite rare.

Dual-functional two-dimensional covalent organic frameworks for water sensing and harvesting

2021 ◽  
Author(s):  
Shan Jiang ◽  
Lingchen Meng ◽  
Wenyue Ma ◽  
Guocui Pan ◽  
Wei Zhang ◽  
...  
Keyword(s):  
Applied Research ◽  
Basic Research ◽  
Adsorption Properties ◽  
Two Dimensional ◽  
Covalent Organic Frameworks ◽  
Dual Functional ◽  
Water Detection ◽  
Water Sensing

Two-dimensional covalent organic frameworks (2D COFs) with both water detection and adsorption properties are highly attractive for both basic research and applied research.

scholarly journals Discovery of Novel Chemical Reactions by Deep Generative Recurrent Neural Network

2020 ◽  
Author(s):  
William Bort ◽  
Igor I. Baskin ◽  
Pavel Sidorov ◽  
Gilles Marcou ◽  
Dragos Horvath ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Chemical Reactions ◽  
Recurrent Neural Network ◽  
Coupling Reactions ◽  
Two Dimensional ◽  
Topographic Map ◽  
Latent Space ◽  
The Given ◽  
Condensed Graph Of Reaction

Here, we report an application of Artificial Intelligence techniques to generate novel chemical reactions of the given type. A sequence-to-sequence autoencoder was trained on the USPTO reaction database. Each reaction was converted into a single Condensed Graph of Reaction (CGR), followed by their translation into on-purpose developed SMILES/GGR text strings. The autoencoder latent space was visualized on the two-dimensional generative topographic map, from which some zones populated by Suzuki coupling reactions were targeted. These served for the generation of novel reactions by sampling the latent space points and decoding them to SMILES/CGR.<br>

A Two‐Dimensional Mesoporous Polypyrrole–Graphene Oxide Heterostructure as a Dual‐Functional Ion Redistributor for Dendrite‐Free Lithium Metal Anodes

2020 ◽  
Vol 132 (29) ◽  
pp. 12245-12251
Author(s):  
Haodong Shi ◽  
Jieqiong Qin ◽  
Kai Huang ◽  
Pengfei Lu ◽  
Chuanfang (John) Zhang ◽  
...  
Keyword(s):  
Graphene Oxide ◽  
Two Dimensional ◽  
Lithium Metal ◽  
Dual Functional ◽  
Lithium Metal Anodes ◽  
Oxide Heterostructure ◽  
Metal Anodes

scholarly journals Research on QR image code recognition system based on artificial intelligence algorithm

2021 ◽  
Vol 30 (1) ◽  
pp. 855-867
Author(s):  
Lina Huo ◽  
Jianxing Zhu ◽  
Pradeep Kumar Singh ◽  
Pljonkin Anton Pavlovich
Keyword(s):  
Artificial Intelligence ◽  
Median Filter ◽  
Recognition Rate ◽  
Distortion Correction ◽  
Deformation Pattern ◽  
Qr Code ◽  
Two Dimensional ◽  
Image Code ◽  
Intelligence Algorithm ◽  
Artificial Intelligence Algorithm

Abstract The QR code recognition often faces the challenges of uneven background fluctuations, inadequate illuminations, and distortions due to the improper image acquisition method. This makes the identification of QR codes difficult, and therefore, to deal with this problem, artificial intelligence-based systems came into existence. To improve the recognition rate of QR image codes, this article adopts an improved adaptive median filter algorithm and a QR code distortion correction method based on backpropagation (BP) neural networks. This combination of artificial intelligence algorithms is capable of fitting the distorted QR image into the geometric deformation pattern, and QR code recognition is accomplished. The two-dimensional code distortion is addressed in this study, which was a serious research issue in the existing software systems. The research outcomes obtained after emphasizing on the preprocessing stage of the image revealed that a significant improvement of 14% is observed for the reading rate of QR image code, after processing by the system algorithm in this article. The artificial intelligence algorithm adopted has a certain effect in improving the recognition rate of the two-dimensional code image.

scholarly journals Visualizing strange metallic correlations in the two-dimensional Fermi-Hubbard model with artificial intelligence

2020 ◽  
Vol 102 (3) ◽  
Author(s):  
Ehsan Khatami ◽  
Elmer Guardado-Sanchez ◽  
Benjamin M. Spar ◽  
Juan Felipe Carrasquilla ◽  
Waseem S. Bakr ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Hubbard Model ◽  
Two Dimensional

scholarly journals An artificial intelligence-aided virtual screening recipe for two-dimensional materials discovery

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Murat Cihan Sorkun ◽  
Séverin Astruc ◽  
J. M. Vianney A. Koelman ◽  
Süleyman Er
Keyword(s):  
Artificial Intelligence ◽  
Virtual Screening ◽  
Two Dimensional ◽  
Two Dimensional Materials

scholarly journals Artificial intelligence enhanced two-dimensional nanoscale nuclear magnetic resonance spectroscopy

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Xi Kong ◽  
Leixin Zhou ◽  
Zhijie Li ◽  
Zhiping Yang ◽  
Bensheng Qiu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Nuclear Magnetic Resonance ◽  
Deep Learning ◽  
Magnetic Resonance ◽  
Single Molecule ◽  
Sparse Matrix ◽  
Nitrogen Vacancy ◽  
Resonance Spectroscopy ◽  
Two Dimensional ◽  
Nuclear Magnetic

Abstract Two-dimensional nuclear magnetic resonance (NMR) is indispensable to molecule structure determination. Nitrogen-vacancy center in diamond has been proposed and developed as an outstanding quantum sensor to realize NMR in nanoscale or even single molecule. However, like conventional multi-dimensional NMR, a more efficient data accumulation and processing method is necessary to realize applicable two-dimensional (2D) nanoscale NMR with a high spatial resolution nitrogen-vacancy sensor. Deep learning is an artificial algorithm, which mimics the network of neurons of human brain, has been demonstrated superb capability in pattern identifying and noise canceling. Here we report a method, combining deep learning and sparse matrix completion, to speed up 2D nanoscale NMR spectroscopy. The signal-to-noise ratio is enhanced by 5.7 ± 1.3 dB in 10% sampling coverage by an artificial intelligence protocol on 2D nanoscale NMR of a single nuclear spin cluster. The artificial intelligence algorithm enhanced 2D nanoscale NMR protocol intrinsically suppresses the observation noise and thus improves sensitivity.

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