Publications by authors named "Masaya Kawamura"
Article Synopsis
- Machine learning is crucial for predicting mechanical properties of polymer materials, which is essential for designing advanced polymers.
- The study introduces a method that utilizes X-ray diffraction (XRD) to analyze higher-order structures of polymers, improving the prediction of their mechanical properties.
- By using Bayesian spectral deconvolution and black-box optimization through Ising machines, the research successfully achieves accurate predictions of seven mechanical properties in polymers, including composite variants.
A quinoline-based hexadentate ligand, (,)-,,,-tetrakis(6-methoxy-2-quinolylmethyl)-1,2-diphenylethylenediamine ((,)-6-MeOTQPhEN), exhibits fluorescence enhancement at 498 nm upon addition of 1 equiv of Zn (/ = 12, φ = 0.047) in aqueous DMF solution (DMF/HO = 2:1). Addition of 1 equiv of Cd affords a much smaller fluorescence increase at the same wavelength (/ = 2.
View Article and Find Full Text PDFEGTA (ethylene glycol bis(2-aminoethyl ether)-N,N,N',N'-tetraacetic acid) and BAPTA (1,2-bis(2-aminophenoxy)ethane-N,N,N',N'-tetraacetic acid) are well-known Ca2+ chelators that have four carboxylates, two nitrogen atoms and two ether oxygen atoms. In the present study, we prepared EGTQ (N,N,N',N'-tetrakis(2-quinolylmethyl)-1,2-bis(2-aminoethoxy)ethane) and BAPTQ (N,N,N',N'-tetrakis(2-quinolylmethyl)-1,2-bis(2-aminophenoxy)ethane) as quinoline alternatives of EGTA and BAPTA, respectively. In methanol-HEPES buffer solution (9 : 1, 50 mM HEPES, 0.
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