Reversible Assembly of Microgels by Metallo-Supramolecular Chemistry

The combination of supramolecular chemistry and soft colloids as microgels represents an ambitious way to develop multi-versatile colloidal assemblies. Hereafter, terpyridine-functionalized poly(N-isopropylacrylamide) (PNiPAM) microgel building blocks are shown to undergo an assemble–freeze–disassemble process. The microgel assemblies, which are controlled by monitoring the attractive and repulsive potentials between the soft colloidal particles, are then frozen by forming inter-particle metal–terpyridine bis-complexes upon addition of the metallic cation (such as Fe^II, Co^II). By oxidation of the metal–terpyridine bis-complex links, the aggregates open up, which is due to the complex dissociation releasing the connected particles in the form of single microgels. We extended our work to the development of 1D filaments and 2D membranes materials made of soft particles connected via supramolecular chemistry.     

Taming the Reactivity of Phosphiranium Salts: Site-Selective C-Centered Ring Opening for Direct Synthesis of Phosphinoethylamines

Advances in the field of phosphorus chemistry are documented, by revealing the synthetic utility of previously underutilized quaternary phosphiranium salts (QPrS) as three-chain-atom electrophilic building blocks. Notably, control of their challenging C-centered electrophilicity is disclosed with an expedient synthesis of tertiary β-anilino phosphines as a proof-of-concept.     

Reversible Assembly of Microgels by Metallo‐Supramolecular Chemistry

The combination of supramolecular chemistry and soft colloids as microgels represents an ambitious way to develop multi‐versatile colloidal assemblies. Hereafter, terpyridine‐functionalized poly(N‐isopropylacrylamide) (PNiPAM) microgel building blocks are shown to undergo an assemble–freeze–disassemble process. The microgel assemblies, which are controlled by monitoring the attractive and repulsive potentials between the soft colloidal particles, are then frozen by forming inter‐particle metal–terpyridine bis‐complexes upon addition of the metallic cation (such as Fe^II, Co^II). By oxidation of the metal–terpyridine bis‐complex links, the aggregates open up, which is due to the complex dissociation releasing the connected particles in the form of single microgels. We extended our work to the development of 1D filaments and 2D membranes materials made of soft particles connected via supramolecular chemistry.     

Sensitivity‐Enhanced 13C‐NMR Spectroscopy for Monitoring Multisite Phosphorylation at Physiological Temperature and pH

Abundant phosphorylation events control the activity of nuclear proteins involved in gene regulation and DNA repair. These occur mostly on disordered regions of proteins, which often contain multiple phosphosites. Comprehensive and quantitative monitoring of phosphorylation reactions is theoretically achievable at a residue‐specific level using ¹H‐¹⁵N NMR spectroscopy, but is often limited by low signal‐to‐noise at pH>7 and T>293 K. We have developed an improved ¹³Cα‐¹³CO correlation NMR experiment that works equally at any pH or temperature, that is, also under conditions at which kinases are active. This allows us to obtain atomic‐resolution information in physiological conditions down to 25 μm . We demonstrate the potential of this approach by monitoring phosphorylation reactions, in the presence of purified kinases or in cell extracts, on a range of previously problematic targets, namely Mdm2, BRCA2, and Oct4.     

Synthetic Organic Design for Solar Fuel Systems

From the understanding of biological processes and metalloenzymes to the development of inorganic catalysts, electro‐ and photocatalytic systems for fuel generation have evolved considerably during the last decades. Recently, organic and hybrid organic systems have emerged to challenge the classical inorganic structures through their enormous chemical diversity and modularity that led earlier to their success in organic (opto)electronics. This Minireview describes recent advances in the design of synthetic organic architectures and promising strategies toward (solar) fuel synthesis, highlighting progress on materials from organic ligands and chromophores to conjugated polymers and covalent organic frameworks.     

Optimization of Hydrogen‐Evolving Photochemical Molecular Devices

A molecular photocatalyst consisting of a Ru^II photocenter, a tetrapyridophenazine bridging ligand, and a PtX₂ (X=Cl or I) moiety as the catalytic center functions as a stable system for light‐driven hydrogen production. The catalytic activity of this photochemical molecular device (PMD) is significantly enhanced by exchanging the terminal chlorides at the Pt center for iodide ligands. Ultrafast transient absorption spectroscopy shows that the intramolecular photophysics are not affected by this change. Additionally, the general catalytic behavior, that is, instant hydrogen formation, a constant turnover frequency, and stability are maintained. Unlike as observed for the Pd analogue, the presence of excess halide does not affect the hydrogen generation capacity of the PMD. The highly improved catalytic efficiency is explained by an increased electron density at the Pt catalytic center, this is confirmed by DFT studies.     

Gelatin‐Alginate Gels and Their Enzymatic Modifications: Controlling the Delivery of Small Molecules

The release of molecules entrapped within biogels is dictated by diffusion laws. Innovative biogel architectures are conceived and tested to control small molecule delivery from gelatin gels. The ionic interactions modulate the release of small molecules. Alginate is then added to gelatin gels and further hydrolyzed; the influence of viscosity is discussed. Next, various mixed gels are compared, such as a gelatin‐alginate IPN and the original architecture of an alginate gel entrapped in a gelatin gel with or without a polysaccharidase. The relative influence of ionic interactions and diffusional constraints on the delivery of small charged molecules is explored, and a solution for controlling diffusion is proposed for any situation.

     

Machine learning in vehicular networking: An overview

As vehicle complexity and road congestion increase, combined with the emergence of electric vehicles, the need for intelligent transportation systems to improve on-road safety and transportation efficiency using vehicular networks has become essential. The evolution of high mobility wireless networks will provide improved support for connected vehicles through highly dynamic heterogeneous networks. Particularly, 5G deployment introduces new features and technologies that enable operators to capitalize on emerging infrastructure capabilities. Machine Learning (ML), a powerful methodology for adaptive and predictive system development, has emerged in both vehicular and conventional wireless networks. Adopting data-centric methods enables ML to address highly dynamic vehicular network issues faced by conventional solutions, such as traditional control loop design and optimization techniques. This article provides a short survey of ML applications in vehicular networks from the networking aspect. Research topics covered in this article include network control containing handover management and routing decision making, resource management, and energy efficiency in vehicular networks. The findings of this paper suggest more attention should be paid to network forming/deforming decision making. ML applications in vehicular networks should focus on researching multi-agent cooperated oriented methods and overall complexity reduction while utilizing enabling technologies, such as mobile edge computing for real-world deployment. Research datasets, simulation environment standardization, and method interpretability also require more research attention.     

Computing the conditioning of the components of a linear least‐squares solution

In this paper, we address the accuracy of the results for the overdetermined full rank linear least‐squares problem. We recall theoretical results obtained in (SIAM J. Matrix Anal. Appl. 2007; 29 (2):413–433) on conditioning of the least‐squares solution and the components of the solution when the matrix perturbations are measured in Frobenius or spectral norms. Then we define computable estimates for these condition numbers and we interpret them in terms of statistical quantities when the regression matrix and the right‐hand side are perturbed. In particular, we show that in the classical linear statistical model, the ratio of the variance of one component of the solution by the variance of the right‐hand side is exactly the condition number of this solution component when only perturbations on the right‐hand side are considered. We explain how to compute the variance–covariance matrix and the least‐squares conditioning using the libraries LAPACK (LAPACK Users' Guide (3rd edn). SIAM: Philadelphia, 1999) and ScaLAPACK (ScaLAPACK Users' Guide. SIAM: Philadelphia, 1997) and we give the corresponding computational cost. Finally we present a small historical numerical example that was used by Laplace (Théorie Analytique des Probabilités. Mme Ve Courcier, 1820; 497–530) for computing the mass of Jupiter and a physical application if the area of space geodesy. Copyright © 2008 John Wiley & Sons, Ltd.