Pyroelectric and piezoelectric scanning microscopy applied to reveal the bipolar state of 4-iodo-4′-nitrobiphenyl (INBP)

Two recent scanning probe techniques were applied to investigate the bipolar twin state of 4-iodo-4′-nitrobiphenyl (INBP) crystals. Solution grown crystals of INBP show typically a morphology which does not express that of a mono-domain polar structure (Fdd2, mm2). From previous X-ray diffraction a twinning volume ratio of [similar]70 : 30 is now explained by two unipolar domains (Flack parameter: 0.075(29)) of opposite orientation of the molecular dipoles, joined by a transition zone showing a width of [similar]140 μm. Scanning pyroelectric microscopy (SPEM) demonstrates a continuous transition of the polarization P from +P into −P across the zone. Application of piezoelectric force microscopy (PFM) confirms unipolar alignment of INBP molecules down to a resolution of [similar]20 nm. A previously proposed real structure for INBP crystals built from lamellae with antiparallel alignment is thus rejected. Anomalous X-ray scattering was used to determine the absolute molecular orientation in the two domains. End faces of the polar axis 2 are thus made up by NO2 groups. Using a previously determined negative pyroelectric coefficient pc leads to a confirmation also by a SPEM analysis. Calculated values for functional group interactions (DA), (AA), (DD) and the stochastic theory of polarity formation allow us to predict that NO2 groups should terminate corresponding faces. Following the present analysis, INBP may represent a first example undergoing dipole reversal upon growth to end up in a bipolar state.

Plotting regression coefficients and other estimates in Stata

Graphical presentation of regression results has become increasingly popular in the scientific literature, as graphs are much easier to read than tables in many cases. In Stata such plots can be produced by the -marginsplot- command. However, while -marginsplot- is very versatile and flexible, it has two major limitations: it can only process results left behind by -margins- and it can only handle one set of results at the time. In this article I introduce a new command called -coefplot- that overcomes these limitations. It plots results from any estimation command and combines results from several models into a single graph. The default behavior of -coefplot- is to plot markers for coefficients and horizontal spikes for confidence intervals. However, -coefplot- can also produce various other types of graphs. The capabilities of -coefplot- are illustrated in this article using a series of examples.

COEFPLOT: Stata module to plot regression coefficients and other results

coefplot plots results from estimation commands or Stata matrices. Results from multiple models or matrices can be combined in a single graph. The default behavior of coefplot is to draw markers for coefficients and horizontal spikes for confidence intervals. However, coefplot can also produce various other types of graphs.

Plotting regression coefficients and other estimates

Graphical display of regression results has become increasingly popular in presentations and in scientific literature because graphs are often much easier to read than tables. Such plots can be produced in Stata by the marginsplot command (see [R] marginsplot). However, while marginsplot is versatile and flexible, it has two major limitations: it can only process results left behind by margins (see [R] margins), and it can handle only one set of results at a time. In this article, I introduce a new command called coefplot that overcomes these limitations. It plots results from any estimation command and combines results from several models into one graph. The default behavior of coefplot is to plot markers for coefficients and horizontal spikes for confidence intervals. However, coefplot can also produce other types of graphs. I illustrate the capabilities of coefplot by using a series of examples.