Dataset Information

Magnetically-induced ferroelectricity in the (ND4)2[FeCl5(D2O)] molecular compound.

ABSTRACT: The number of magnetoelectric multiferroic materials reported to date is scarce, as magnetic structures that break inversion symmetry and induce an improper ferroelectric polarization typically arise through subtle competition between different magnetic interactions. The (NH4)2[FeCl5(H2O)] compound is a rare case where such improper ferroelectricity has been observed in a molecular material. We have used single crystal and powder neutron diffraction to obtain detailed solutions for the crystal and magnetic structures of (NH4)2[FeCl5(H2O)], from which we determined the mechanism of multiferroicity. From the crystal structure analysis, we observed an order-disorder phase transition related to the ordering of the ammonium counterion. We have determined the magnetic structure below TN, at 2?K and zero magnetic field, which corresponds to a cycloidal spin arrangement with magnetic moments contained in the ac-plane, propagating parallel to the c-axis. The observed ferroelectricity can be explained, from the obtained magnetic structure, via the inverse Dzyaloshinskii-Moriya mechanism.

SUBMITTER: Alberto Rodriguez-Velamazan J

PROVIDER: S-EPMC4586439 | biostudies-literature | 2015 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Magnetically-induced ferroelectricity in the (ND4)2[FeCl5(D2O)] molecular compound.

Alberto Rodríguez-Velamazán José J Fabelo Óscar Ó Millán Ángel Á Campo Javier J Johnson Roger D RD Chapon Laurent L

Scientific reports 20150929

The number of magnetoelectric multiferroic materials reported to date is scarce, as magnetic structures that break inversion symmetry and induce an improper ferroelectric polarization typically arise through subtle competition between different magnetic interactions. The (NH4)2[FeCl5(H2O)] compound is a rare case where such improper ferroelectricity has been observed in a molecular material. We have used single crystal and powder neutron diffraction to obtain detailed solutions for the crystal a ...[more]

PMID: 26417890

Similar Datasets

Project description:Pharmaceutical or phytopharmaceutical molecules rely on the interaction with one or more specific molecular targets to induce their anticipated biological responses. Nonetheless, these compounds are also prone to interact with many other non-intended biological targets, also known as off-targets. Unfortunately, off-target identification is difficult and expensive. Consequently, QSAR models predicting the activity on a target have gained importance in drug discovery or in the de-risking of chemicals. However, a restricted number of targets are well characterized and hold enough data to build such in silico models. A good alternative to individual target evaluations is to use integrative evaluations such as transcriptomics obtained from compound-induced gene expression measurements derived from cell cultures. The advantage of these particular experiments is to capture the consequences of the interaction of compounds on many possible molecular targets and biological pathways, without having any constraints concerning the chemical space. In this work, we assessed the value of a large public dataset of compound-induced transcriptomic data, to predict compound activity on a selection of 69 molecular targets. We compared such descriptors with other QSAR descriptors, namely the Morgan fingerprints (similar to extended-connectivity fingerprints). Depending on the target, active compounds could show similar signatures in one or multiple cell lines, whether these active compounds shared similar or different chemical structures. Random forest models using gene expression signatures were able to perform similarly or better than counterpart models built with Morgan fingerprints for 25% of the target prediction tasks. These performances occurred mostly using signatures produced in cell lines showing similar signatures for active compounds toward the considered target. We show that compound-induced transcriptomic data could represent a great opportunity for target prediction, allowing to overcome the chemical space limitation of QSAR models.

Dataset Information

Magnetically-induced ferroelectricity in the (ND4)2[FeCl5(D2O)] molecular compound.

Publications

Magnetically-induced ferroelectricity in the (ND4)2[FeCl5(D2O)] molecular compound.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets