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Summary The combination of noninvasive compositional analysis by Raman microspectrometry with high-resolution imaging in the scanning electron microscope greatly expands the analytical capabilities of the electron microscope. However, the chemical preparation of scanning electron microscope (SEM) specimens, although adequate for low-resolution imaging of superficial detail, is not the true representation of the chemistry and composition of the sample, as extraction and aggregation artefacts as a result of dehydrating and cross-linking agents are abundant. The original chemical composition and ultrastructure is only preserved using cryo preparation methods. Therefore, a complete cryo transfer flange was designed and built to add cryogenic control of specimens to the configuration of the EMRAM instrument, a combined Raman spectrometer and XL-30 ESEM instrument. The Raman spectra of two model specimen, polystyrene beads and 2.3M sucrose were studied at ambient and cryogenic temperatures as well as during a heating ramp. Comparing the fingerprint regions of polystyrene and sucrose, both measured at ambient and at cryogenic conditions, only small spectral differences were observed for the main peaks of both molecules. A pronounced sharpening of the bands occurred in the 800–400 cm−1 region, a result of the reduction of intermolecular interactions. The enhanced visibility of the lower frequency modes may offer interesting potential for more detailed interpretation of Raman spectra.

Summary The stereological estimation of second-order descriptors of spatial architecture appears to be inherently more time-consuming and labour-intensive than the estimation of first-order quantities (total quantities or ratios). Therefore, far fewer researchers tend to make use of second-order approaches in their stereological research projects. In this paper, we use a tutorial approach to illustrate how a desire for practical simplicity has provided us with a data collection method that can be used to simultaneously estimate both first-order and second-order properties of the microstructure of a defined anatomical feature of an organ. The approach does not rely on new results from theory, but nevertheless allows either isotropic uniform random or vertical uniform random sections to be used to make estimates of a comprehensive list of 10 microstructural parameters using relationships that are well known in the literature. The probe used in all cases is an isotropically distributed Ruler and the data collection protocol is easy to learn and apply. We illustrate the method on brain tissue but emphasize that the approach can also be applied to non-biological material.

Highly connected continuous phases in a porous medium can usefully be described in terms of their connectivity. Appropriate descriptions of connectivity are the connectivity number density or the related quantity Euler number density. Broadly speaking, Euler number density is equal to the number of discrete objects which comprise the phase plus the number of enclosed cavities within those objects less the number of tunnels through the objects, per unit volume. The conneulor is a convenient stereological method for estimating Euler number density. The relevant probes (i.e. disectors) from which the estimate is obtained are randomly positioned and arbitrary orientated pairs of closely spaced parallel planes. We have applied the conneulor method to estimate the Euler number density of the pore space in a cell of sintered silica imaged using a magnetic resonance (MR) microscope. By flooding the cell simultaneously with a mixture of oil and water prior to imaging we were able to obtain a series of 16 contiguous perfectly registered serial MR sections depicting the pore space. The Euler number density of the porous medium estimated from four systematic pairs of consecutive MR images is −5.6 per mm3 with a coefficient of error (CE) of 10–15%. The Euler number density was lower near the walls of the silica cell than the centre, which is consistent with the method of formation of the cell. In contrast to the majority of stereological methods, and in common with the physical disector, the conneulor method requires that deductions are made regarding the changes in the morphology of the structure of interest that occur between the two parallel image planes comprising the sampling probe. We therefore compared estimates of the Euler number density of the pore space obtained by two independent observers. We conclude that careful thought and discussion between observers is required before a definitive set of counts is made. Once mastered the conneulor counting rules are straightforward to apply.

In multiphase systems consisting of ‘particles’ embedded in a matrix the three-dimensional spatial distribution of the particles may represent important structural information. In systems where the matrix is transparent or translucent recent developments in microscopy allow the three-dimensional location of particles to be recorded. Using these data a spatial statistical, or second-order stereological, analysis can be carried out. In second-order stereology functions of interparticle distances are used as summary statistics of the spatial distributions. These statistics show whether the particles are randomly arranged or, more commonly, either clustered together or inhibited from close approach to each other. This paper focuses on the estimation of one of these spatial statistics, the nearest-neighbour distance distribution function or G-function. In practice, estimation of the G-function is plagued by an ‘edge-effect’ bias introduced by the sampling process itself. There exist a number of G-function estimators that tackle this edge effect problem; for single sample ‘bricks’ it can be shown that these estimators become increasingly accurate as the brick size increases, i.e. they are consistent. However, in many practical cases the size of a sampling brick is fixed by experimental constraints and in these circumstances the only way to increase sample size is to take replicated sampling regions. In this paper we review a number of existing G-function estimators and propose a new estimator. These estimators are compared using the criterion of how well they overcome the edge-effect when they are applied to replicated samples of a fixed size of brick. These comparisons were made using Monte-Carlo simulation methods; the results show that three existing estimators are clearly unsuitable for estimating the G-function from replicated sample bricks. Of the other estimators the recommended estimator depends upon the number of replicates taken; however, we conclude that if a total of more than about 800 points are analysed then the bias in the pooled estimate of the G-function can be reduced to tolerable levels.