We have characterized the dissolution state of microcrystalline cellulose (MCC) in aqueous tetrabutylammonium hydroxide, TBAH(aq), at different concentrations of TBAH, by means of turbidity and small-angle X-ray scattering. The solubility of cellulose increases with increasing TBAH concentration, which is consistent with solubilization driven by neutralization. When comparing the two polymorphs, the solubility of cellulose I is higher than that of cellulose II. This has the consequence that the dissolution of MCC (cellulose I) may create a supersaturated solution with respect to cellulose II. As for the dissolution state of cellulose, we identify three different regimes. (i) In the stable regime, corresponding to concentrations below the solubility of cellulose II, cellulose is molecularly dissolved and the solutions are thermodynamically stable. (ii) In the metastable regime, corresponding to lower supersaturations with respect to cellulose II, a minor aggregation of cellulose occurs and the solutions are kinetically stable. (iii) In the unstable regime, corresponding to larger supersaturations, there is macroscopic precipitation of cellulose II from solution. Finally, we also discuss strong alkali solvents in general and compare TBAH(aq) with the classical NaOH(aq) solvent.
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http://dx.doi.org/10.1098/rsos.170487 | DOI Listing |
Stat Med
February 2025
Department of Biostatistics and Computational Biology, University of Rochester, Rochester, NY.
Clinical trials are often designed based on limited information about effect sizes and precision parameters with risks of underpowered studies. This is more problematic for SMARTs where strategy effects are based on sequences of treatments. Sample size adjustment offers flexibility through re-estimating sample size during the trial to ensure adequate power at the final analysis.
View Article and Find Full Text PDFGenome Biol Evol
January 2025
Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA 15219.
Homology is a key concept underpinning the comparison of sequences across organisms. Sequence-level homology is based on a statistical framework optimized over decades of work. Recently, computational protein structure prediction has enabled large-scale homology inference beyond the limits of accurate sequence alignment.
View Article and Find Full Text PDFComput Biol Med
January 2025
Department of Mechanical Engineering, Stanford University, United States.
We present a built-in physics neural network architecture, known as inelastic Constitutive Artificial Neural Network (iCANN), to discover the inelastic phenomenon of tensional homeostasis. In this course, identifying the optimal model and material parameters to accurately capture the macroscopic behavior of inelastic materials can only be accomplished with significant expertise, is often time-consuming, and prone to error, regardless of the specific inelastic phenomenon. To address this challenge, built-in physics machine learning algorithms offer significant potential.
View Article and Find Full Text PDFACS Photonics
January 2025
Institute of Electronic Structure and Laser, Foundation for Research and Technology-Hellas (FORTH-IESL), GR-70013 Heraklion, Crete, Greece.
THz metamaterials present unique opportunities for next-generation technologies and applications as they can fill the "THz gap" originating from the weak response of natural materials in this regime, providing a variety of novel or advanced electromagnetic wave control components and systems. Here, we propose a novel metamaterial design made of three-dimensional, metallic, "cactus-like" meta-atoms, showing electromagnetically induced transparency (EIT) and enhanced refractive index sensing performance at low THz frequencies. Following a detailed theoretical analysis, the structure is realized experimentally using multiphoton polymerization and electroless silver plating.
View Article and Find Full Text PDFNatl Sci Rev
February 2025
Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei 230026, China.
Ion exchange membranes (IEMs) enable fast and selective ion transport and the partition of electrode reactions, playing an important role in the fields of precise ion separation, renewable energy storage and conversion, and clean energy production. Traditional IEMs form ion channels at the nanometer-scale via the assembly of flexible polymeric chains, which are trapped in the permeability/conductivity and selectivity trade-off dilemma due to a high swelling propensity. New-generation IEMs have shown great potential to break this intrinsic limitation by using microporous framework channels for ion transport under a confinement regime.
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