A zinc(ii) coordination polymer {[Zn3(mtrb)3(btc)2]·3H2O}n (1) was synthesized and characterized (mtrb = 1,3-bis(1,2,4-triazole-4-ylmethyl)benzene, btc = 1,3,5-benzenetricarboxylate). The polymer 1 shows an unusual (3,4,4)-coordinated self-catenated 3D network with the point symbol of {63}2{62·82·102}{64·82}2. The polymer 1 is the first luminescent sensor for the detection of 2-amino-4-nitrophenol (ANP). The polymer 1 is also a good luminescence sensor for detection of TNP, 2,4-DNP, 4-NP, ANP and 2-NP in MeOH, particularly for TNP. The order of detection efficiency is TNP > 2,4-DNP > 4-NP > ANP > 2-NP. The polymer 1 also exhibits high sensitivity and selectivity as a luminescence sensor for the detection of Fe3+, Cr2O72- and CrO42- in aqueous solution. Our experiments showed that the presence of interfering ions had no significant effect on the sensing of Fe3+, Cr2O72- or CrO42- ions. The detection limits for TNP, ANP, Fe3+, Cr2O72- and CrO42- are 0.22 μM, 4.12 μM, 1.78 μM, 2.83 μM, and 4.52 μM, respectively. The luminescence sensor is stable and can be recycled for detection at least five times. The possible quenching mechanisms are discussed. The polymer 1 is also an effective photocatalyst for degradation of methylene blue (MB) under visible or UV light irradiation.

Reconstructive solid-state transformations are followed by significant changes in the system of chemical bonds, i.e. in the topology of the substance. Understanding these mechanisms at the atomic level is crucial for proper explanation and prediction of chemical reactions and phase transitions in solids and, ultimately, for the design of new materials. Modeling of solid-state transitions by geometrical, molecular dynamics or quantum-mechanical methods does not account for topological transformations. As a result, the chemical nature of the transformation processes are overlooked, which limits the predictive power of the models. We propose a universal model based on network representation of extended structures, which treats any reorganization in the solid state as a network transformation. We demonstrate this approach rationalizes the configuration space of the solid system and enables prediction of new phases that are closely related to already known phases. Some new phases and unclear transition pathways are discovered in example systems including elementary substances, ionic compounds and molecular crystals.

The search for new materials requires effective methods for scanning the space of atomic configurations, in which the number is infinite. Here we present an extensive application of a topological network model of solid-state transformations, which enables one to reduce this infinite number to a countable number of the regions corresponding to topologically different crystalline phases. We have used this model to successfully generate carbon allotropes starting from a very restricted set of initial structures; the generation procedure has required only three steps to scan the configuration space around the parents. As a result, we have obtained all known carbon structures within the specified set of restrictions and discovered 224 allotropes with lattice energy ranging in 0.16–1.76 eV atom−1 above diamond including a phase, which is denser and probably harder than diamond. We have shown that this phase has a quite different topological structure compared to the hard allotropes from the diamond polytypic series. We have applied the tiling approach to explore the topology of the generated phases in more detail and found that many phases possessing high hardness are built from the tiles confined by six-membered rings. We have computed the mechanical properties for the generated allotropes and found simple dependences between their density, bulk, and shear moduli.

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