Stability and Hydrolyzation of Metal Organic Frameworks with Paddle-Wheel SBUs upon Hydration

Instability of most prototypical metal organic frameworks (MOFs) in the presence of moisture is always a limita- tion for industrial scale development. In this work, we examine the dissociation mechanism of microporous paddle wheel frameworks M(bdc)(ted)0.5 [M=Cu, Zn, Ni, Co; bdc= 1,4-benzenedicarboxylate; ted= triethylenediamine] in controlled humidity environments. Combined in-situ IR spectroscopy, Raman, and Powder x-ray diffraction measurements show that the stability and modification of isostructual M(bdc)(ted)0.5 compounds upon exposure to water vapor critically depend on the central metal ion. A hydrolysis reaction of water molecules with Cu-O-C is observed in the case of Cu(bdc)(ted)0.5. Displacement reactions of ted linkers by water molecules are identified with Zn(bdc)(ted)0.5 and Co(bdc)(ted)0.5. In contrast,. Ni(bdc)(ted)0.5 is less suscept- ible to reaction with water vapors than the other three compounds. In addition, the condensation of water vapors into the framework is necessary to initiate the dissociation reaction. These findings, supported by supported by first principles theoretical van der Waals density functional (vdW-DF) calculations of overall reaction enthalpies, provide the necessary information for de- termining operation conditions of this class of MOFs with paddle wheel secondary building units and guidance for developing more robust units

All-Optical QPSK Pattern Recognition in High-Speed Optoelectronic Firewalls

In the 5G era, more sensitive information will be transmitted on optical networks. Although modern cryptography could support different security mechanisms in different layers, the optical layer has not been paid enough attention but it is also vulnerable to attack by tapping or other methods. The optoelectronic firewall is one of the promising security strategies which can accelerate the operating speed within manageable costs by employing optical signal processing compared with current electronic firewalls. The most significant and challenging component of an optoelectronic firewall is pattern recognition. However, either the latest pattern recognition systems can only process the OOK or BPSK modulation format signals, or the coherent correlators supporting higher order modulation format require advanced components to deal with a higher number of symbols. In this paper, to address the pattern recognition challenges, we propose, analyze and simulate a pattern recognition system of QPSK signals integrated into all-optical high-speed optoelectronic firewalls used for optical layer security. The numerical simulation results first demonstrate the feasibility, and a baud rate of 100GBaud can be achieved. Next, a threshold-setting method is developed which also proves the ability to recognize arbitrary target patterns. Then some noise analysis results demonstrate that the pattern recognition of the I branch can be immune to the noise while the output power of EDFAs in the Q branch needs to be configured carefully. Finally, the matched result of a real Ethernet frame further reveals that our proposed system is promisingly applied in optoelectronic firewalls

Graph Transformer with Convolution Parallel Networks for Predicting Single and Binary Component Adsorption Performance of Metal–Organic Frameworks

Metal–organic frameworks (MOFs) are considered one of the most important materials for carbon capture and storage (CCS) due to the advantages of porosity, multifunction, diverse structure, and controllable chemical composition. With the continuous development of artificial intelligence (AI) technology, more and more machine learning models are used to identify MOFs with high performance within a massive search space. However, current works have yet to form a model that uses graph-structured data only, which can predict the adsorption properties of single and binary components. In this work, we proposed and developed a graph transformer, combined with convolution parallel networks, called GC-Trans. The model can accurately and efficiently predict the adsorption performance of MOFs under the single- and binary-component adsorption conditions using only the features of the crystal diagram as inputs. By extracting and fusing local and global feature information, the model has stronger expression and generalization abilities. Thus, we used it to screen the ARC-MOF database and analyze the MOF structures that meet the target requirements. Additionally, to demonstrate the transferability of the model, we applied transfer learning methods to predict the CO2/CH4 separations and CH4 uptake, both of which showed good predictive performance

Graph Transformer with Convolution Parallel Networks for Predicting Single and Binary Component Adsorption Performance of Metal–Organic Frameworks

Metal–organic frameworks (MOFs) are considered one of the most important materials for carbon capture and storage (CCS) due to the advantages of porosity, multifunction, diverse structure, and controllable chemical composition. With the continuous development of artificial intelligence (AI) technology, more and more machine learning models are used to identify MOFs with high performance within a massive search space. However, current works have yet to form a model that uses graph-structured data only, which can predict the adsorption properties of single and binary components. In this work, we proposed and developed a graph transformer, combined with convolution parallel networks, called GC-Trans. The model can accurately and efficiently predict the adsorption performance of MOFs under the single- and binary-component adsorption conditions using only the features of the crystal diagram as inputs. By extracting and fusing local and global feature information, the model has stronger expression and generalization abilities. Thus, we used it to screen the ARC-MOF database and analyze the MOF structures that meet the target requirements. Additionally, to demonstrate the transferability of the model, we applied transfer learning methods to predict the CO2/CH4 separations and CH4 uptake, both of which showed good predictive performance

R-isophthalic Acid-based Coordination Polymers (R = Hydrogen or Bromine)

Three new R-isophthalic acid-based (R = H or Br) coordination polymers have been designed and synthesized. By changing the N-containing ligand in the system, we are able to tune the dimensionality of coordination polymers from one-dimension (1D) to two-dimensions (2D) with the same basic building unit. Also, different metal ions can be incorporated into the same structures. Compound 1 [Cu(bipa)(py)2]·0.5(H2O) (H2bipa = 5-bromoisophthalic acid; py = pyridine) and compound 2 [Co(bipa)(py)2] are 1D chain structures. Compound 3 [Cu8(ipa)8(bpe)8]·2(bpe)·4(H2O) (bpe=1,2-bis(4-pyridyl)ethane) is a 2D layered structure

Graph Transformer with Convolution Parallel Networks for Predicting Single and Binary Component Adsorption Performance of Metal–Organic Frameworks

Metal–organic frameworks (MOFs) are considered one of the most important materials for carbon capture and storage (CCS) due to the advantages of porosity, multifunction, diverse structure, and controllable chemical composition. With the continuous development of artificial intelligence (AI) technology, more and more machine learning models are used to identify MOFs with high performance within a massive search space. However, current works have yet to form a model that uses graph-structured data only, which can predict the adsorption properties of single and binary components. In this work, we proposed and developed a graph transformer, combined with convolution parallel networks, called GC-Trans. The model can accurately and efficiently predict the adsorption performance of MOFs under the single- and binary-component adsorption conditions using only the features of the crystal diagram as inputs. By extracting and fusing local and global feature information, the model has stronger expression and generalization abilities. Thus, we used it to screen the ARC-MOF database and analyze the MOF structures that meet the target requirements. Additionally, to demonstrate the transferability of the model, we applied transfer learning methods to predict the CO2/CH4 separations and CH4 uptake, both of which showed good predictive performance