The CISORP Water Sorption Analyser has been used to characterise a selection of solid samples at relative humidities from
0 to 100% and at ambient pressure. The analysis reveals many interesting features about the samples and shows the scope of
Hysteresis due to porosity and differences in the physical properties of similar chemical samples show up clearly in isotherm
curves. Kinetic curves reveal features such as the level of stability of dehydrated food products, changes in the hydration
states of salts, and the effect of adding powdered excipient on the water sorption behaviour of a pharmaceutical compound.
Kinetic curves were also used to compare the water sorption behaviour of two types of wood found inside a pine cone, and to
determine equilibrium moisture sorption by calculation.
It was shown that many samples take up moisture irreversibly under the experimental conditions such as amorphous sucrose and
other freeze-dried samples, as well as unstable crystalline forms of compounds. Wet samples such as soaked brick and archaeological
wood from a well dry out irreversibly even at 100% RH.
Recording isotherms at different temperatures allows the calculation of enthalpies of water sorption. If these are compared
with the enthalpy of water condensation the two processes can be compared quantitatively.
The Unified Grain Moisture Algorithm is
capable of improved accuracy and allows the combination of many grain types
into a single “unified calibration”. The purposes of this research were to
establish processes for determining unifying parameters from the chemical and
physical properties of grains. The data used in this research were obtained as
part of the United States Department of Agriculture-Grain Inspection, Packers
and Stockyards Administration's Annual Moisture Calibration Study. More than
5,000 grain samples were tested with a Hewlett-Packard 4291A Material/Impedance
Analyzer. Temperature tests were done with a Very High Frequency prototype
system at Corvinus University of Budapest. Typical chemical and physical
parameters for each of the major grain types were obtained from the literature.
Data were analyzed by multivariate chemometric methods. One of the most
important unifying parameters (Slope) and the temperature correction
coefficient were successfully modeled. The Offset and Translation unifying
parameters were not modeled successfully, but these parameters can be estimated
relatively easily through limited grain tests.
Authors:M. B. Djurdjevic, J. H. Sokolowski, and Z. Odanovic
The dendrite coherency point (DCP) temperature refers to the state of a solidifying alloy at which a coherent dendrite network is established during the formation of grains. Several relatively complex methods for detection of the DCP temperature have been developed. There are four main DCP temperature testing approaches: (i) the rheological technique, (ii) thermal analysis of the minimum temperature difference between two cooling curves, (iii) thermal analysis of the second derivative of one cooling curve, and (iv) the thermal diffusivity measurement technique. This paper follows up the proposed thermal analysis of one center cooling curve for the determination of the DCP characteristics such as: temperature, time, instantaneous solidification rate, and fraction solid. The first derivative of the cooling curve is plotted versus the temperature and time and the thermal characteristics of all metallurgical reactions, including the DCP are determined with the same accuracy achieved using the two thermocouple technique developed by Bäckerud et al. [, ]. Statistical analysis of the DCP temperature using the one versus two thermocouple techniques shows R2 equal to 0.99. This research revealed that utilization of dT/dt versus the temperature curve methodology also allows for analysis of the α-Al dendrite nucleation and growth characteristics and consequent determination of the grain size. On-going work on this new methodology for characterization of other solidification events will be presented in subsequent papers.
Authors:Tímea Kaszab, Lídia Bornemisza, and Katalin Badak-Kerti
Profile Analysis (TPA) test by TA-XTplus (Stable Micro System, Surrey, UK) Texture Analyzer with P/25 type stainless steel cylinder at room temperature. Test setting were as follows: compression with pre-test speed 2 mm/s, test speed 1 mm/s, post
temperaturetest. Fan et al. [ 10 ] performed laboratory tests on eight square sections to study the resistance of stainless-steel box columns under high temperature. Neural network is a new method that has been applied for elaborating heat data, but it never
Actually, the temperaturetested in the experiment is not the sample's real temperature, because the released heat not only contributed to the sample itself but also to the bombs of the sample. Assuming that the heat released from the reaction is used to
Authors:G. A. A. Teixeira, A. S. Maia, I. M. G. Santos, A. L. Souza, A. G. Souza, and N. Queiroz
The values of T onset and T P , obtained in the TMDSC cooling curves of the biodiesel samples, showed a correspondence with the values of the low-temperaturetests, determined by conventional methods, Fig. 4 . The values of CP (5–12 °C) and CFPP (4
Authors:E. Radomińska, T. Znamierowska, and W. Szuszkiewicz
composition of the sinters was identified by X-ray powder diffraction at room-temperature. Testing procedure showed that all samples of the system of interest melted in excess of 1400 °C. Accordingly, to draw the liquidus curves the samples after pressing into
Authors:Javier A. Díaz-Ponce, Eugenio A. Flores, Alfonso Lopez-Ortega, Jose G. Hernández-Cortez, Arquimides Estrada, Laura V. Castro, and Flavio Vazquez
of the first cycle regarding the sample's stability, and to corroborate the peak's position. These low-temperaturetests are non-destructive; several measurements can be performed by using the same sample, considering that at 30 °C, the thermal