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.     

Functional Carbazole Liquid‐Crystal Block Codendrimers with Optical and Electronic Properties

The synthesis and characterisation of a family of block codendrimers consisting of highly versatile mesogenic and carbazole‐containing 2,2‐bis(hydroxymethyl)propionic acid (bis‐MPA) dendrons are reported. The liquid‐crystal behaviour was investigated by means of differential scanning calorimetry, polarised‐light optical microscopy and X‐ray diffraction. Depending on the chemical structure of the constituent dendrons, the codendrimers show lamellar or columnar mesophases. On the basis of the experimental results, models both at the molecular level and in the mesophase are proposed. The physical properties of the block codendrimers derived from the presence of the carbazole moiety in their structure were investigated: photoluminescence in solution and in the mesophase, electrochemical behaviour and hole transport. Electrodeposition of carbazole dendrons afforded a globular supramolecular conformation in which the mesogenic molecular side plays a key role.     

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.