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We have measured the magnetic susceptibility and heat capacity of CuO nanoparticles (16 nm) from 2 K to 400 K using a Quantum Design Physical Properties Measurement System (PPMS). The magnetization curves, acquired at various field strengths from 0 Oe to 50 kOe, are similar to literature data of bulk and nanoscale CuO showing a minimum at about 150 K, an upturn as temperature approaches 0 K, and a broad maximum at high temperatures extending beyond 400 K. The heat capacity data between 200 K and 400 K show several broad peaks. The number of these peaks and the temperatures at which they occur differ significantly from the magnetic transitions known to exist in bulk CuO. To further investigate these transitions in nanoscale CuO, we performed temperature dependent x-ray diffraction (XRD) at temperatures from 90 K to 700 K from which lattice parameters as a function of temperature were derived using a Rietveld refinement. Although no phase transitions were observed in these data, changes in the slopes of the lattice parameters are apparent at the transition temperatures observed in the heat capacity and susceptibility measurements. The various transitions in the heat capacity data are attributed to competing ferromagnetic and antiferromagnetic interactions caused by structural properties that are unique to nanophase CuO, and the temperature range of multiferroicity in nanoscale CuO is shown to extend to temperatures higher than those observed for bulk CuO. The heat capacity and temperature dependent XRD measurements are the first of their kind to be reported for CuO nanoparticles.

The location and function of dopants in metal oxide nanoparticles have been poorly characterized for many systems. We have performed heat capacity measurements, electron energy-loss spectroscopy (EELS), and X-ray diffraction (XRD) on 10 TiO2 nanoparticle samples that have different amounts of Al dopant to determine the location and function of the Al3+ cations. From the heat capacity data, lattice vacancies are observed to increase significantly with the addition of the Al dopant, suggesting Al3+ cations enter the TiO2 lattice and create vacancies due to the charge difference between Al3+ and Ti4+. The presence of gapped terms in fits of the low-temperature heat capacity data also suggests that small regions of short-range order are created within the TiO2 lattice. Entropies at T = 298.15 K were determined from the heat capacity data and show effects related to the entropy of mixing, suggesting that a solid solution of Al/TiO2 is formed. EELS data confirm that Al enters the TiO2 lattice but also indicates...

The thermodynamic properties of hydrated α-Fe2O3 (hematite) and Fe3O4 (magnetite) nanoparticles have been comprehensively assessed. In addition to 9 nm Fe3O4, three α-Fe2O3 nanoparticles samples of different sizes (11, 14, and 25 nm) and bulk α-Fe2O3 have been evaluated by inelastic neutron scattering methods. The contribution of the two-level magnetic spin flip transition to the heat capacity of the α-Fe2O3 particles has been determined. The isochoric heat capacity of the water confined on the surface of these two types of iron oxide particles have been calculated from their INS spectra, and is affected by the chemical composition of the underlying particle. Furthermore, the heat capacity and dynamics of the particle hydration layers appear to be influenced by a complex array of factors including particle size, water coverage, and possibly the magnetic state of the particle itself.

We report an efficient, general methodology for producing high-surface area metal oxide nanomaterials for a vast range of metal oxides, including at least one metal oxide nanomaterial from nearly every transition metal and semi-metal group in the periodic table (groups 3–4 and 6–15) as well as several from the lanthanide group (see Table 1). The method requires only 2–3 simple steps; a hydrated metal salt (usually a nitrate or chloride salt) is ground with bicarbonate (usually NH4HCO3) for 10–30 minutes to form a precursor that is then either untreated or rinsed before being calcined at relatively low temperatures (220–550 °C) for 1–3 hours. The method is thus similar to surfactant-free aqueous methods such as co-precipitation but is unique in that no solvents are added. The resulting “solvent-deficient” environment has interesting and unique consequences, including increased crystallinity of the products over other aqueous methods and a mesoporous nature in the inevitable agglomerates. The products are chemically pure and phase pure with crystallites generally 3–30 nm in average size that aggregate into high surface area, mesoporous agglomerates 50–300 nm in size that would be useful for catalyst and gas sensing applications. The versatility of products and efficiency of the method lend its unique potential for improving the industrial viability of a broad family of useful metal oxide nanomaterials. In this paper, we outline the methodology of the solvent-deficient method using our understanding of its mechanism, and we describe the range and quality of nanomaterials it has produced thus far.

Porous metal oxide nanoparticles is a new class of material of great scientific and technological importance with a wide range of applications. In this article, we briefly review the synthetic methods and thermodynamic properties of such materials. We compare and summarize common synthetic routes of such materials including solid-state, solution- phase (co-precipitation, sol-gel, microemulsion, solvothermal/hydrothermal, non-aqueous), and vapor-phase methods. As for the thermodynamics of porous metal oxide nanoparticles, we review experimental determinations, mainly by calorime- try, on surface and interfaces energetics. The interplay among particle size, surface area, morphology, surface stabilizer, phase stability, and redox potentials is discussed.

Traditional Screening Designs, such as resolution III 2k-p fractional factorials are used routinely in the initial stages of process development. These designs are used to determine which process variables have the largest effect on process outcomes. Once a screening design is complete and the data are analysed, follow-up experiments are normally required in order to develop useful prediction equations involving the important variables and to identify the optimal process operating conditions. Recently developed definitive screening designs allow researchers to identify important variables and optimum process conditions after one set of experiments, eliminating the need for follow-up experiments. This leads to the question: What is now the role of traditional and definitive screening designs in process optimisation? We share our insights gained from using both of these designs in developing a process to produce catalyst support material to shed light on these questions.