Structural Origins of Crack Resistance on Magnesium Aluminoborosilicate Glasses Studied by Solid-State NMR

Bradtmüller, Henrik; Uesbeck, Tobias; Eckert, Hellmut; Murata, Tetsuya; Nakane, Shingo; Yamazaki, Hiroki

doi:10.1021/acs.jpcc.9b03600

Cited by 30 publications

(63 citation statements)

References 62 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…9 Overall, the trends observed in the present study are in excellent agreement with previous results obtained on other aluminoborosilicate glasses. [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23] Figure 8a shows the relationship between ( 27 Al) and  of all the glasses of the present study. Except for the Na-Ca and Na-Mg 〈 〉 glasses (yellow symbols), there seems to be a good correlation.…”

Section: Accepted Articlesupporting

confidence: 93%

“…As previously shown the only related glass revealing significant concentrations of higher-coordinate aluminum in both the TQMAS-and the SATRAS data is the purely MgO containing sample 60SiO 2 -20Al 2 O 3 -20MgO. 9 In that sample, the amount of higher-coordinated aluminum is estimated at 16±2% Al(V) and 2±2% Al(VI). Minor amounts of Al(V) have also been detected in 60SiO 2 -20Al 2 O 3 -10MgO-10Na 2 O glass.…”

Section: Accepted Articlementioning

confidence: 63%

“…This article is protected by copyright. All rights reserved 10Al 2 O 3 -10B 2 O 3 -10Na 2 O-10MO (M = Mg, 9 and M = Ca, Sr, Ba) and 60SiO 2 -10Al 2 O 3 -10B 2 O 3 -(20-x)MO-xMgO (M = Ca, Sr, Ba), combining results from 11 B, 27 Al, and 29 Si solid state NMR with physical property measurements. 116 pm (crystal ionic radii as listed in ref, 29 ) we calculate ionic potentials of 3.72, 2.81, 2.42, 2.15, and 1.38 ×10 -9 C/m, for Mg 2+ , Ca 2+ , Sr 2+ , Ba 2+ , and Na + , respectively.…”

Section: Accepted Articlementioning

confidence: 96%

“…To monitor the structural origins of this evolution, nuclear magnetic resonance has already provided some important insights into this matter. [4][5][6]8,9 In aluminosilicate and aluminoborate glasses, crack initiation resistance was shown to increase with increasing average coordination number (presence of higher-coordinated aluminum species). 5,7 In the alkaline-earth aluminoborate glasses of reference 8 exactly these structural changes have been monitored.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Structure‐property relations in crack‐resistant alkaline‐earth aluminoborosilicate glasses studied by solid state NMR

et al. 2021

Self Cite

View full text Add to dashboard Cite

The effect of the average ionic potential ξ = Ze/r of the network modifier cations on crack initiation resistance (CR) and Young's modulus E has been measured for a series of alkaline‐earth aluminoborosilicate glasses with the compositions 60SiO2–10Al2O3–10B2O3–(20−x)M(2)O–xM’O (0 ≤ x ≤ 20; M, M’ = Mg, Ca, Sr, Ba, Na). Systematic trends indicating an increase of CR with increasing ionic potential, ξ, have been correlated with structural properties deduced from the NMR interaction parameters in 29Si, 27Al, 23Na, and 11B solid state NMR. 27Al NMR spectra indicate that the aluminum atoms in these glasses are essentially all four‐coordinated, however, the average quadrupolar coupling constant extracted from lineshape analysis increases linearly with increasing average ion potential computed from the cation composition. A similar linear correlation is observed for the average 29Si chemical shift, whereas the fraction of four‐coordinate boron decreases linearly with increasing ξ. Altogether the results indicate that in pure alkaline‐earth boroaluminosilicate glasses the crack resistance/E‐modulus trade‐off can be tailored by the alkaline‐earth oxide inventory. In contrast, the situation looks more complicated in glasses containing both Na2O and the alkaline‐earth oxides MgO, CaO, SrO, and BaO. For 60SiO2–10Al2O3–10B2O3–10MgO–10Na2O glass, the NMR parameters, interpreted in the context of their correlations with ionic potentials, are consistent with a partial network former role of the MgO component, enhancing crack resistance. Altogether the presence of MgO in aluminoborosilicate glasses helps overcome the trade‐off issue between high crack resistance and high elasticity modulus present in borosilicate glasses, thereby offering additional opportunities for the design of glasses that are both very rigid and very crack resistant.

show abstract

Section: Accepted Articlesupporting

confidence: 93%

Section: Accepted Articlementioning

confidence: 63%

Section: Accepted Articlementioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Structure‐property relations in crack‐resistant alkaline‐earth aluminoborosilicate glasses studied by solid state NMR

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…These observations could explain the difference in the mechanical property between MAS and NAS. That is, MAS has much higher CR than that of NAS (ie, MAS: 2,200 gf and NAS: 300 gf) 2 . In addition, at initial deformation stage, MAS flows significantly comparing with NAS at the same shear stress condition (eg, 0.08-and 0.5-hour dwell of S1.1 + M0.25 in Table 1).…”

Section: Mg-aluminosilicate Glassmentioning

confidence: 99%

Transition in deformation mechanism of aluminosilicate glass at high pressure and room temperature

et al. 2020

View full text Add to dashboard Cite

Deformation experiments for 20(MgO or Na 2 O)-20Al 2 O 3-60SiO 2 glasses were performed in simple shear geometry at 1.5-5 GPa and room temperature. An abrupt change in the thinning rate and the turning of the birefringence azimuth at a shear strain of γ = 1-2 indicate a transition of deformation mechanism from uniaxial compression aided by densification to shear flow in the glasses. The high-dense magne-How to cite this article: Osada K, Yamada A, Ohuchi T, Yoshida S, Matsuoka J. Transition in deformation mechanism of aluminosilicate glass at high pressure and room temperature.

show abstract

Accelerating NMR Shielding Calculations Through Machine Learning Methods: Application to Magnesium Sodium Silicate Glasses

Bertani,

Pedone,

Faglioni

et al. 2024

ChemPhysChem

View full text Add to dashboard Cite

In this work, we have applied the Kernel Ridge Regression (KRR) method using a Least Square Support Vector Regression (LSSVR) approach for the prediction of the NMR isotropic magnetic shielding (σiso) of active nuclei (17O, 23Na, 25Mg, and 29Si) in a series of (Mg, Na) – silicate glasses. The Machine Learning (ML) algorithm has been trained by mapping the local environment of each atom described by the Smooth Overlap of Atomic Position (SOAP) descriptor with isotropic chemical shielding values computed with DFT using the Gauge‐Included‐Projector‐Augmented‐Wave (GIPAW) approach. The influence of different training datasets generated through molecular dynamics simulations at various temperatures and with different inter‐atomic potentials has been tested and we demonstrate the importance of a wide exploration of the configurational space to enhance the transferability of the ML‐regressor. Finally, the trained ML‐regressor has been used to simulate the 29Si MAS NMR spectra of systems containing up to 20000 atoms by averaging hundreds of configurations extracted from classical MD simulations to account for thermal vibrations. This ML approach is a powerful tool for the interpretation of NMR spectra using relatively large systems at a fraction of the computational time required by quantum mechanical calculations which are of high computational cost.

show abstract

Structural Origins of Crack Resistance on Magnesium Aluminoborosilicate Glasses Studied by Solid-State NMR

Cited by 30 publications

References 62 publications

Structure‐property relations in crack‐resistant alkaline‐earth aluminoborosilicate glasses studied by solid state NMR

Structure‐property relations in crack‐resistant alkaline‐earth aluminoborosilicate glasses studied by solid state NMR

Transition in deformation mechanism of aluminosilicate glass at high pressure and room temperature

Accelerating NMR Shielding Calculations Through Machine Learning Methods: Application to Magnesium Sodium Silicate Glasses

Contact Info

Product

Resources

About