Simultaneously enhancing the strength, ductility and conductivity of copper matrix composites with graphene nanoribbons

The incorporation of low-dimensional nanomaterials into 3D metal matrices are promising to translate their intriguing properties from nanoscale to the macroscopic world. However, the design of robust nanofillers and effective fabrication of such bulk composites remain challenging. Here we report a configuration design of nanocarbon for reinforcing metals via unzipping carbon nanotubes (CNTs) into graphene nanoribbons (GNRs), which are novel quasi-1D carboneous nanomaterials combining elegantly the properties of graphene nanosheets and CNTs, to provide insight into the viability to retrieve good plasticity and conductivity that defy the boundaries of classical composites. We realize an optimal balance between elevated yield strength and impressively larger plastic deformation coupled with a simultaneous improving of electrical conductivity (216 MPa, 8.0% and 54.89 MS m−1, i.e., 1.55 folds, 130.4% and 105% of the matrix, respectively), by highlighting that the excellent intrinsic properties, strong interfacial bonding, optimized orientation control and especially the unique geometric factors of GNRs are conducive to transmitting stress from Cu matrix without sacrificing the ductility and electrical conductance. This work provides a new vista on the integration and interaction of novel low-dimensional nanofillers with bulk 3D metal matrices

Poly[aqua­bis(μ4-naphthalene-1,4-di­carboxyl­ato)(1,10-phenanthroline-5,6-dione)dimanganese(II)]

The three-dimensional coordination polymer, [Mn2(C12H6O4)2(C12H6N2O2)(H2O)]n, features a water-coord­inated MnII ion and an N-heterocycle-chelated MnII ion, both in six-coordinate octa­hedral geometries. Of the two rigid dianions, one is bonded to four MnII ions, with each of the O atoms being connected to a different metal ion. The other dianion uses one carboxyl­ate group to chelate to one MnII ion and its other carboxyl­ate group to bind to two MnII ions

catena-Poly[[(1,10-phenanthroline-5,6-dione-κ2 N,N′)lead(II)]-μ-terephthalato-κ2 O 1:O 4]

The PbII atom in the polymeric title compound, [Pb(C8H4O4)(C12H6N2O2)]n, is chelated by the N-heterocycle, and adjacent atoms are bridged by rigid terephthalate dianions into a linear chain. The PbII atom is stereochemically active in a ψ-square-pyramidal coordination geometry in which the lone-pair electrons occupy a basal site. When three other weaker Pb⋯O inter­actions are considered, the geometry is a ψ-dodeca­hedron

Bis(2-methyl-1H-imidazole-κN 3)silver(I) nitrate dihydrate

The AgI atom in the salt, [Ag(C4H6N2)2]NO3·2H2O, shows a nearly linear coordination [N—Ag—N = 178.26 (7)°]. The cation, anion and water mol­ecules are linked by N—H⋯O and O—H⋯O hydrogen bonds into a layer motif extending parallel to (101)

Simultaneously improving the mechanical and electrical properties of poly(vinyl alcohol) composites by high-quality graphitic nanoribbons

Although carbon nanotubes (CNTs) have shown great potential for enhancing the performance of polymer matrices, their reinforcement role still needs to be further improved. Here we implement a structural modification of multi-walled CNTs (MWCNTs) to fully utilize their fascinating mechanical and electrical properties via longitudinal splitting of MWCNTs into graphitic nanoribbons (GNRs). This nanofiller design strategy is advantageous for surface functionalization, strong interface adhesion as well as boosting the interfacial contact area without losing the intrinsic graphitic structure. The obtained GNRs have planar geometry, quasi-1D structure and high-quality crystallinity, which outperforms their tubular counterparts, delivering a superior load-bearing efficiency and conductive network for realizing a synchronous improvement of the mechanical and electrical properties of a PVA-based composite. Compared to PVA/CNTs, the tensile strength, Young’s modulus and electrical conductivity of the PVA/GNR composite at a filling concentration of 3.6 vol.% approach 119.1 MPa, 5.3 GPa and 2.4 × 10−4 S m−1, with increases of 17%, 32.5% and 5.9 folds, respectively. The correlated mechanics is further rationalized by finite element analysis, the generalized shear-lag theory and the fracture mechanisms

Leaf-like carbon nanotube-graphene nanoribbon hybrid reinforcements for enhanced load transfer in copper matrix composites

A leaf-inspired nanoengineering is employed for the structural design of carbon nanofillers. We fabricate leaf-like carbon nanotube (CNT)-graphene nanoribbon (GNR) hybrids as novel reinforcements for a copper matrix composite. The straight and stiff CNT ‘midribs’ are conducive to individual dispersion whilst the two-dimensional GNR ‘margins’ provide more sufficient interface contact area and deformation gradient zone, giving rise to significantly improved interfacial load transfer and mechanical strength as compared to the unmodified nanotubes. The mechanics and strengthening mechanisms are further rationalized by finite element analysis and the generalized shear-lag theory

What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams

Open domain question answering (OpenQA) tasks have been recently attracting more and more attention from the natural language processing (NLP) community. In this work, we present the first free-form multiple-choice OpenQA dataset for solving medical problems, MedQA, collected from the professional medical board exams. It covers three languages: English, simplified Chinese, and traditional Chinese, and contains 12,723, 34,251, and 14,123 questions for the three languages, respectively. We implement both rule-based and popular neural methods by sequentially combining a document retriever and a machine comprehension model. Through experiments, we find that even the current best method can only achieve 36.7\%, 42.0\%, and 70.1\% of test accuracy on the English, traditional Chinese, and simplified Chinese questions, respectively. We expect MedQA to present great challenges to existing OpenQA systems and hope that it can serve as a platform to promote much stronger OpenQA models from the NLP community in the future.Comment: Submitted to AAAI 202

Ammonium benzene­phospho­nate

In the crystal structure of the title salt, NH4 +.[(C6H5)P(O)2(OH)]− or NH4 +·C6H6O3P−, the N and O atoms inter­act via hydrogen bonds to generate a layer motif. The phenyl rings are stacked above and below this layer, sandwiching the hydrogen-bonded layer