Synthesis and applications of graphene oxide aerogels in bone tissue regeneration: a review

Development of biocompatible porous supports is a promising strategy in the field of tissue engineering for the repair and regeneration of bone tissues with severe damage. Graphene oxide aerogels (GOAs) are excellent candidates for the manufacture of these systems due to their porosity, ability to imitate bone structure, and mechanical resistance, and according to their surface chemical reactivity, they can facilitate osseointegration, osteogenesis, osteoinduction and osteoconduction. In this review, synthesis of GOAs from the most primary source is described, and recent studies on the use of these functionalized carbonaceous foams as scaffolding for bone tissue regeneration are presented.     

Solid Echo in Three-Spin Groups with Arbitrary Constants of Dipole–Dipole Interaction

Applied Magnetic Resonance - The suggested approach allowed us to derive analytical expressions for modeling the shape of solid-echo signal and its time evolution for a system of three-spin groups...     

Mechanistic Aspects of a Highly Active Dinuclear Zinc Catalyst for the Co‐polymerization of Epoxides and CO2

The dinuclear zinc complex reported by us is to date the most active zinc catalyst for the co‐polymerization of cyclohexene oxide (CHO) and carbon dioxide. However, co‐polymerization experiments with propylene oxide (PO) and CO₂ revealed surprisingly low conversions. Within this work, we focused on clarification of this behavior through experimental results and quantum chemical studies. The combination of both results indicated the formation of an energetically highly stable intermediate in the presence of propylene oxide and carbon dioxide. A similar species in the case of cyclohexene oxide/CO₂ co‐polymerization was not stable enough to deactivate the catalyst due to steric repulsion.     

Local linear convergence analysis of Primal–Dual splitting methods

In this paper, we study the local linear convergence properties of a versatile class of Primal–Dual splitting methods for minimizing composite non-smooth convex optimization problems. Under the assumption that the non-smooth components of the problem are partly smooth relative to smooth manifolds, we present a unified local convergence analysis framework for these methods. More precisely, in our framework, we first show that (i) the sequences generated by Primal–Dual splitting methods identify a pair of primal and dual smooth manifolds in a finite number of iterations, and then (ii) enter a local linear convergence regime, which is characterized based on the structure of the underlying active smooth manifolds. We also show how our results for Primal–Dual splitting can be specialized to cover existing ones on Forward–Backward splitting and Douglas–Rachford splitting/ADMM (alternating direction methods of multipliers). Moreover, based on these obtained local convergence analysis result, several practical acceleration techniques are discussed. To exemplify the usefulness of the obtained result, we consider several concrete numerical experiments arising from fields including signal/image processing, inverse problems and machine learning. The demonstration not only verifies the local linear convergence behaviour of Primal–Dual splitting methods, but also the insights on how to accelerate them in practice.