The hexagonal phase of LuFeO 3 is a rare example of a multiferroic material possessing a weak ferromagnetic moment, which is predicted to be switchable by an electric field. We stabilize this structure in bulk form though Mn and Sc doping, and determine the complete magnetic and crystallographic structures using neutron-scattering and magnetometry techniques. The ferroelectric P 6 3 cm space group is found to be stable over a wide concentration range, ordering antiferromagnetically with Néel temperatures that smoothly increase following the ratio of c to a (c/a) lattice parameters up to 172 K, the highest found in this class of materials to date. The magnetic structure for a range of temperatures and dopings is consistent with recent studies of high quality epitaxial films of pure hexagonal LuFeO 3 including a ferromagnetic moment parallel to the ferroelectric axis. We propose a mechanism by which room-temperature multiferroicity could be achieved in this class of materials.

Free-standing, composite hydrogels containing the visible-light responsive metal-free semiconductor graphitic carbon nitride (g-C 3 N 4 ) as an integral component have been fabricated by direct casting techniques. At 0.67% g-C 3 N 4 loading, intermolecular interactions between the semiconductor particles and the PVA polymer chains enhance both the mechanical and photophysical properties of the resulting hydrogels. In contrast, much higher g-C 3 N 4 loadings of 3.3 or 6.7% g-C 3 N 4 resulted in growth of the average semiconductor particle size and reduction in interactions between the incorporated photocatalyst and the PVA chains. The increased dimensions of the g-C 3 N 4 semiconductor particles had the effect of compromising the mechanical properties of the composite system and reducing the lifetime of photogenerated charge carriers. However, the close proximity of g-C 3 N 4 particles that is realized at increased semiconductor loading densities improves the absorption cross section of the material, resulting in an overall improvement in the photocatalytic activity of the material. Application of visible radiation caused all of the composite hydrogels to generate hydrogen peroxide (H 2 O 2 ) at catalytic rates of 0.9−2.5 μM/min, while H 2 O 2 decomposition rates remained similar across the different preparations. In studies to examine antimicrobial performance, irradiation of 6.7% g-C 3 N 4 /PVA hydrogel samples with visible radiation (400 ≤ λ ≤ 800 nm) generated sufficient H 2 O 2 to significantly reduce both the viable planktonic cell population and biofilm formation in cultures of Pseudomonas aeruginosa. KEYWORDS: g-C 3 N 4 , hydrogel, H 2 O 2 , antibiofilm, antibacterial

We consider families of codes obtained by "lifting" a base code C through operations such as k-XOR applied to "local views" of codewords of C, according to a suitable k-uniform hypergraph. The k-XOR operation yields the direct sum encoding used in works of [Ta-Shma, STOC 2017] and [Dinur and Kaufman, FOCS 2017].

Rietveld refinement of X‐ray and neutron diffraction patterns is routinely used to solve crystal and magnetic structures of organic and inorganic materials over many length scales. Despite its success over the past few decades, conventional Rietveld analysis suffers from tedious iterative methodologies, and the unfortunate consequence of many least‐squares algorithms discovering local minima that are not the most accurate solutions. Bayesian methods which allow the explicit encoding of a priori knowledge pose an attractive alternative to this approach by enhancing the ability to determine the correlations between parameters and to provide a more robust method for model selection. Global approaches also avoid the divergences and local minima often encountered by practitioners of the traditional Rietveld technique. The goal of this work is to demonstrate the effectiveness of an automated Bayesian algorithm for Rietveld refinement of neutron diffraction patterns in the solution of crystallographic and magnetic structures. A new software package, BLAND (Bayesian library for analyzing neutron diffraction data), based on the Markov–Chain Monte Carlo minimization routine, is presented. The benefits of such an approach are demonstrated through several examples and compared with traditional refinement techniques.

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