Sandra Galmarini

Bio: Sandra Galmarini is an academic researcher from Swiss Federal Laboratories for Materials Science and Technology. The author has contributed to research in topics: Portlandite & Aerogel. The author has an hindex of 11, co-authored 17 publications receiving 2857 citations. Previous affiliations of Sandra Galmarini include École Polytechnique Fédérale de Lausanne & École Polytechnique.

Additive manufacturing of silica aerogels.

Abstract: Owing to their ultralow thermal conductivity and open pore structure1-3, silica aerogels are widely used in thermal insulation4,5, catalysis6, physics7,8, environmental remediation6,9, optical devices10 and hypervelocity particle capture11. Thermal insulation is by far the largest market for silica aerogels, which are ideal materials when space is limited. One drawback of silica aerogels is their brittleness. Fibre reinforcement and binders can be used to overcome this for large-volume applications in building and industrial insulation5,12, but their poor machinability, combined with the difficulty of precisely casting small objects, limits the miniaturization potential of silica aerogels. Additive manufacturing provides an alternative route to miniaturization, but was "considered not feasible for silica aerogel"13. Here we present a direct ink writing protocol to create miniaturized silica aerogel objects from a slurry of silica aerogel powder in a dilute silica nanoparticle suspension (sol). The inks exhibit shear-thinning behaviour, owing to the high volume fraction of gel particles. As a result, they flow easily through the nozzle during printing, but their viscosity increases rapidly after printing, ensuring that the printed objects retain their shape. After printing, the silica sol is gelled in an ammonia atmosphere to enable subsequent processing into aerogels. The printed aerogel objects are pure silica and retain the high specific surface area (751 square metres per gram) and ultralow thermal conductivity (15.9 milliwatts per metre per kelvin) typical of silica aerogels. Furthermore, we demonstrate the ease with which functional nanoparticles can be incorporated. The printed silica aerogel objects can be used for thermal management, as miniaturized gas pumps and to degrade volatile organic compounds, illustrating the potential of our protocol.

The Atomic-Level Structure of Cementitious Calcium Aluminate Silicate Hydrate

Abstract: Despite use of blended cements containing significant amounts of aluminum for over 30 years, the structural nature of aluminum in the main hydration product, calcium aluminate silicate hydrate (C-A-S-H), remains elusive. Using first-principles calculations, we predict that aluminum is incorporated into the bridging sites of the linear silicate chains and that at high Ca:Si and H2O ratios, the stable coordination number of aluminum is six. Specifically, we predict that silicate-bridging [AlO2(OH)4]5- complexes are favored, stabilized by hydroxyl ligands and charge balancing calcium ions in the interlayer space. This structure is then confirmed experimentally by one- and two-dimensional dynamic nuclear polarization enhanced 27Al and 29Si solid-state NMR experiments. We notably assign a narrow 27Al NMR signal at 5 ppm to the silicate-bridging [AlO2(OH)4]5- sites and show that this signal correlates to 29Si NMR signals from silicates in C-A-S-H, conflicting with its conventional assignment to a "third aluminate hydrate" (TAH) phase. We therefore conclude that TAH does not exist. This resolves a long-standing dilemma about the location and nature of the six-fold-coordinated aluminum observed by 27Al NMR in C-A-S-H samples.

Fast parallel algorithms for short-range molecular dynamics

Abstract: Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.