Inorganic-organic hybrids of the p,p'-diphenylmethylenediphosphinate ligand with bivalent metals: a new 2D-layered phenylphosphinate zinc(II) complex

By reaction of Zn(CH₃COO)₂ with p,p′-diphenylmethylenediphosphinic acid in water a new inorganic–organic polymeric hybrid of formula [Zn(CH₂(P(Ph)O₂)₂)] has been synthesized and completely characterized. The X-ray analysis established that the structure consists of 2D-layered polymeric array, the 2D-sheets being built up through strong covalent linkages between the zinc metal and the oxygen donors of the phenylphosphinate ligand. The 2D-layers, which are featuring a mesh-net fashion, present voids of various dimensionality, up to 24-membered rings. The organic parts of the hybrid ligand, namely the phenyl rings, are shielding the inorganic skeleton of the layers, preventing the propagation of the polymer in the third dimension. No water molecules are present in the lattice, both of coordination and crystallization. Crystal data are: monoclinic, P2₁Ic, a=11.840(2), b=9.646(9), c=12.516(5) Å, β=95.03(2), V=1423.9(15) ų, Z=4. The solid material has been characterized by ³¹P MAS NMR spectroscopy and thermogravimetric analysis.

Inorganic-organic hybrids of the p,p'-diphenylmethylenediphosphinate, pcp2-. synthesis, characterization and XRPD structures of [Sn(pcp)] and [Cu(pcp)]

Two new inorganic-organic polymeric hybrids [Sn(pcp)] and [Cu(pcp)], pcp = CH₂(PhPO₂)₂^2-, have been synthesized and structurally chracterized. The tin derivative has been obtained by reaction of the p,p'-diphenylmethylenediphosphinic acid (H₂pcp) in water with SnCl₂·2H₂O, while the copper derivative has been synthesized through a hydrothermal reaction from the same H₂pcp acid and Cu(O₂CMe)₂·H₂O. The structures of these compounds have been solved "ab initio" by X-ray powder diffraction (XRPD) data. [Sn(pcp)] has a ladder-like polymeric structure, with tin(II) centers bridged by diphenylmethylenediphosphinate ligands, and alternating six- and eight-membered rings. The hemilectic coordination around the metal shows the tin(II) lone pair to be operative, resulting in significant interaction mainly with a C-C bond of one phenyl ring. The [Cu(pcp)] complex displays a polymeric columnar structure formed by two intersecting sinusoidal ribbons of copper(II) ions bridged by the bifunctional phosphinate ligands. The intersections of the ribbons are made of dimeric units of pentacoordinated copper ions. Crystal data for [Sn(pcp)]: monoclinic, space group P2₁Ic, a = 11.2851(1), b = 15.4495(6), c = 8.6830(1) Å, ^β= 107.546(1)°, V = 1443.44(9) Å, Z = 4. Crystal data for [Cu(pcp)]: triclinic, space group P, a = 10.7126(4), b = 13.0719(4), c = 4.9272(3) Å, α= 92.067(5), β= 95.902(7), γ= 87.847(4)°, V = 685.47(7), Z = 2. The tin compound has been characterized by ¹¹⁹Sn MAS NMR (magic-angle spinning NMR), revealing asymmetry in the valence electron cloud about tin. Low-temperature magnetic measurements of the copper compound have indicated the presence of weak antiferromagnetic interactions below 50 K.

Comparative study of materials-binding peptide interactions with gold and silver surfaces and nanostructures : A thermodynamic basis for biological selectivity of inorganic materials

Controllable 3D assembly of multicomponent inorganic nanomaterials by precisely positioning two or more types of nanoparticles to modulate their interactions and achieve multifunctionality remains a major challenge. The diverse chemical and structural features of biomolecules can generate the compositionally specific organic/inorganic interactions needed to create such assemblies. Toward this aim, we studied the materials-specific binding of peptides selected based upon affinity for Ag (AgBP1 and AgBP2) and Au (AuBP1 and AuBP2) surfaces, combining experimental binding measurements, advanced molecular simulation, and nanomaterial synthesis. This reveals, for the first time, different modes of binding on the chemically similar Au and Ag surfaces. Molecular simulations showed flatter configurations on Au and a greater variety of 3D adsorbed conformations on Ag, reflecting primarily enthalpically driven binding on Au and entropically driven binding on Ag. This may arise from differences in the interfacial solvent structure. On Au, direct interaction of peptide residues with the metal surface is dominant, while on Ag, solvent-mediated interactions are more important. Experimentally, AgBP1 is found to be selective for Ag over Au, while the other sequences have strong and comparable affinities for both surfaces, despite differences in binding modes. Finally, we show for the first time the impact of these differences on peptide mediated synthesis of nanoparticles, leading to significant variation in particle morphology, size, and aggregation state. Because the degree of contact with the metal surface affects the peptide's ability to cap the nanoparticles and thereby control growth and aggregation, the peptides with the least direct contact (AgBP1 and AgBP2 on Ag) produced relatively polydispersed and aggregated nanoparticles. Overall, we show that thermodynamically different binding modes at metallic interfaces can enable selective binding on very similar inorganic surfaces and can provide control over nanoparticle nucleation and growth. This supports the promise of bionanocombinatoric approaches that rely upon materials recognition.

Identifying inorganic material affinity classes for peptide sequences based on context learning

There is a growing interest in identifying inorganic material affinity classes for peptide sequences due to the development of bionanotechnology and its wide applications. In particular, a selective model capable of learning cross-material affinity patterns can help us design peptide sequences with desired binding selectivity for one inorganic material over another. However, as a newly emerging topic, there are several distinct challenges of it that limit the performance of many existing peptide sequence classification algorithms. In this paper, we propose a novel framework to identify affinity classes for peptide sequences across inorganic materials. After enlarging our dataset by simulating peptide sequences, we use a context learning based method to obtain the vector representation of each amino acid and each peptide sequence. By analyzing the structure and affinity class of each peptide sequence, we are able to capture the semantics of amino acids and peptide sequences in a vector space. At the last step we train our classifier based on these vector features and the heuristic rules. The construction of our models gives us the potential to overcome the challenges of this task and the empirical results show the effectiveness of our models.