pith. sign in

arxiv: 1906.10111 · v1 · pith:UDAODKKCnew · submitted 2019-06-24 · ❄️ cond-mat.mtrl-sci · cond-mat.dis-nn· cond-mat.soft

Cooling Rate Effects on the Structure of 45S5 Bioglass: Computational and Experimental Evidence of Si--P Avoidance

Pratik Bhaskar , Yashasvi Maurya , Rajesh Kumar , R. Ravinder , Amarnath R. Allu , Sumanta Das , Nitya Nand Gosvami , Randall E. Youngman
show 5 more authors
Mikkel S. B{\o}dker Nerea Mascaraque Morten M. Smedskjaer Mathieu Bauchy N. M. Anoop Krishnan
This is my paper

Pith reviewed 2026-05-25 17:10 UTC · model grok-4.3

classification ❄️ cond-mat.mtrl-sci cond-mat.dis-nncond-mat.soft
keywords 45S5 bioglasscooling ratemolecular dynamicsMAS-NMRSi-P avoidanceglass structurebioactivity
0
0 comments X

The pith

MD simulations of 45S5 bioglass match MAS-NMR data only after extrapolation to experimental cooling rates, revealing Si-P avoidance.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper examines how the atomic structure of 45S5 bioglass changes with cooling rate during quenching. Molecular dynamics simulations run at high rates are compared to magic angle spinning nuclear magnetic resonance measurements on laboratory samples. Agreement appears only when the simulation data are extrapolated downward to the orders-of-magnitude slower rates used in experiments. This match establishes a Si-P avoidance pattern in the glass network that the authors link to the material's ability to bond with living tissue.

Core claim

The central claim is that the non-equilibrium structure of 45S5 bioglass contains a clear preference against Si-O-P linkages. This Si-P avoidance emerges once molecular dynamics results obtained at high cooling rates are extrapolated to the lower rates of actual glass formation, bringing the simulated network statistics into line with experimental MAS-NMR spectra and thereby resolving earlier mismatches between computation and measurement.

What carries the argument

Si-P avoidance, the observed preference against direct silicon-phosphorus connections through oxygen in the glass network.

If this is right

  • Bioactivity of 45S5 can be adjusted by changing the cooling rate to alter the degree of Si-P avoidance.
  • Prior structural models of the glass must be revised to include thermal-history dependence.
  • The validated extrapolation method allows future simulations to predict structures at practical cooling rates.
  • Ion-release kinetics during dissolution are expected to depend on the final Si-P connectivity set by cooling rate.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same avoidance rule may govern network formation in other bioactive glass compositions containing both silicon and phosphorus.
  • Manufacturing processes that impose different cooling rates could produce batches with measurably different tissue-bonding performance.
  • Direct bioactivity tests on samples cooled at several controlled rates would test whether avoidance strength correlates with bonding speed.
  • Computational glass models could incorporate an explicit cooling-rate parameter to improve transferability to experiment.

Load-bearing premise

That molecular dynamics results at high cooling rates can be extrapolated reliably to the much slower cooling rates of laboratory glass preparation.

What would settle it

MAS-NMR spectra taken on 45S5 samples prepared at an intermediate cooling rate between the simulation and experiment values would show a Si-O-P linkage fraction inconsistent with the linear extrapolation trend.

Figures

Figures reproduced from arXiv: 1906.10111 by Amarnath R. Allu, Mathieu Bauchy, Mikkel S. B{\o}dker, Morten M. Smedskjaer, Nerea Mascaraque, Nitya Nand Gosvami, N. M. Anoop Krishnan, Pratik Bhaskar, Rajesh Kumar, Randall E. Youngman, R. Ravinder, Sumanta Das, Yashasvi Maurya.

Figure 2
Figure 2. Figure 2: Total pair distribution functions in the glassy state for the studied cooling rates computed by MD simulations. (a) (b) [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
read the original abstract

Due to its ability to bond with living tissues upon dissolution, 45S5 bioglass and related compositions are promising materials for the replacement, regeneration, and repair of hard tissues in the human body. However, the details of their atomic structure remain unclear. This is partially due to the non-equilibrium nature of glasses, as their non-crystalline structure is highly dependent on their thermal history, namely, the cooling rate used during quenching. Herein, using molecular dynamics (MD) simulations and magic angle spinning nuclear magnetic resonance (MAS-NMR) spectroscopy experiments, we investigate the structure of the nominal 45S5 bioglass composition prepared using cooling rates ranging over several orders of magnitude. We show that the simulations results are in very good agreement with experimental data, provided that they are extrapolated toward lower cooling rates achieved in experiments. These results highlight that previously reported inconsistencies between simulations and experiments stem from the difference in cooling rate, thereby addressing one of the longstanding questions on the structure of bioglass. Based on these results, we demonstrate the existence of a Si--P avoidance behavior, which may be key in controlling the bioactivity of 45S5 bioglass.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript uses MD simulations of 45S5 bioglass across a range of high cooling rates combined with MAS-NMR experiments to argue that structural metrics (Q^n populations and Si-O-P linkages) extrapolated to laboratory cooling rates (~1 K/s) match experimental data, thereby resolving prior sim-exp discrepancies and establishing Si-P avoidance as a key feature controlling bioactivity.

Significance. If the extrapolation is robust, the work would usefully reconcile computational and experimental views of bioglass structure and identify a structural motif relevant to dissolution and bioactivity. The multi-rate MD approach and direct comparison to NMR are strengths; the claim is falsifiable via the linearity test.

major comments (2)
  1. [Results] Results section (extrapolation plots): the central claim requires that Q^n and Si-O-P fractions vary linearly with log(cooling rate) over the simulated range before extrapolation across >10 orders of magnitude; the manuscript must show the raw data points, regression slopes, R^2 values, and any curvature diagnostics to confirm the functional form used for the intercept at experimental rates.
  2. [Methods] Methods/Results (structural descriptors): the definitions and counting procedures for Si-O-P linkages and Q^n speciation from the MD trajectories are load-bearing for the avoidance claim; without explicit formulas or pseudocode it is impossible to verify that the reported avoidance is not an artifact of the chosen cutoff or averaging window.
minor comments (2)
  1. [Abstract] Abstract: the phrase 'very good agreement' should be accompanied by a quantitative metric (e.g., mean absolute deviation on Q^n fractions) rather than a qualitative statement.
  2. [Figures] Figure captions: cooling-rate values and the precise temperature range over which each trajectory was quenched should be stated explicitly in every relevant figure.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed comments, which help strengthen the manuscript. We address each major point below and will revise the manuscript accordingly.

read point-by-point responses

  1. Referee: [Results] Results section (extrapolation plots): the central claim requires that Q^n and Si-O-P fractions vary linearly with log(cooling rate) over the simulated range before extrapolation across >10 orders of magnitude; the manuscript must show the raw data points, regression slopes, R^2 values, and any curvature diagnostics to confirm the functional form used for the intercept at experimental rates.

    Authors: We agree that explicit verification of linearity is essential to support the extrapolation. In the revised manuscript we will add the raw data points for all Q^n populations and Si-O-P fractions plotted against log(cooling rate), together with the linear regression parameters (slopes, intercepts, R^2 values) and curvature diagnostics (residual plots and a test for quadratic terms). These will appear in the Results section or as a supplementary figure to allow readers to assess the functional form directly. revision: yes

  2. Referee: [Methods] Methods/Results (structural descriptors): the definitions and counting procedures for Si-O-P linkages and Q^n speciation from the MD trajectories are load-bearing for the avoidance claim; without explicit formulas or pseudocode it is impossible to verify that the reported avoidance is not an artifact of the chosen cutoff or averaging window.

    Authors: We acknowledge that the current manuscript lacks sufficient detail on these procedures. In the revised version we will expand the Methods section to include explicit formulas: (i) the distance cutoffs used to define Si-O and P-O bonds, (ii) the exact definition of Q^n speciation based on the number of bridging oxygens attached to each Si atom, and (iii) the counting algorithm for Si-O-P linkages (shared oxygen atoms between Si and P tetrahedra). We will also supply pseudocode for the post-processing routines so that the avoidance metric can be reproduced unambiguously. revision: yes

Circularity Check

0 steps flagged

Independent MD trajectories and external MAS-NMR data compared via extrapolation; no definitional or fitted-input circularity

full rationale

The paper computes structural descriptors (Q^n populations, Si–O–P linkages) from MD runs at high cooling rates, extrapolates the trend versus log(cooling rate), and compares the extrapolated values to separate MAS-NMR measurements on laboratory glasses. The Si–P avoidance conclusion is drawn from the observed under-population of Si–O–P linkages relative to a random model, not from any parameter fitted to the target experimental data. No self-citation is load-bearing for the central claim, and the derivation does not reduce any reported quantity to itself by construction. The linearity assumption in the extrapolation is an independent modeling choice whose validity can be tested against additional data, but it does not create circularity within the reported chain.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review; no explicit free parameters, axioms, or invented entities are identifiable. The claim rests on the unstated premise that standard MD glass models remain valid when extrapolated across many orders of magnitude in cooling rate.

pith-pipeline@v0.9.0 · 5830 in / 1166 out tokens · 30442 ms · 2026-05-25T17:10:56.777504+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

3 extracted references · 3 canonical work pages

  1. [1]

    Stoor, E

    P. Stoor, E. Söderling, J.I. Salonen, Antibacterial effects of a bioactive glass paste on oral microorganisms, Acta Odontologica Scandinavica. 56 (1998) 161–165. doi:10.1080/000163598422901. [5] S. Srinivasan, R. Jayasree, K.P. Chennazhi, S.V. Nair, R. Jayakumar, Biocompatible alginate/nano bioactive glass ceramic composite scaffolds for periodontal tissu...

    work page doi:10.1080/000163598422901 1998

  2. [2]

    Lane, Cooling rate and stress relaxation in silica melts and glasses via microsecond molecular dynamics, Physical Review E

    J.M.D. Lane, Cooling rate and stress relaxation in silica melts and glasses via microsecond molecular dynamics, Physical Review E. 92 (2015) 012320. doi:10.1103/PhysRevE.92.012320. [25] G. Agnello, A.N. Cormack, Coordination state and defect evolution in vitreous silica structures formed using Molecular Dynamics under variable cooling conditions, Journal ...

    work page doi:10.1103/physreve.92.012320 2015

  3. [3]

    Mauro, R.J

    J.C. Mauro, R.J. Loucks, A.K. Varshneya, P.K. Gupta, Enthalpy landscapes and the glass transition, in: S. Yip, T.D. de la Rubia (Eds.), Scientific Modeling and Simulations, Springer Netherlands, 2008: pp. 241–281. http://link.springer.com/chapter/10.1007/978-1-4020-9741-6_15 (accessed August 14, 2016). [43] H. Liu, S. Dong, L. Tang, N.M. Anoop Krishnan, E...

    work page doi:10.1007/978-1-4020-9741-6_15 2008