Quantifying superparamagnetic signatures in nanoparticle magnetite: a generalized approach for physically meaningful statistics and synthesis diagnostics.

Magnetization is a common measurable for characterizing bulk, nanoscale, and molecular materials, which can be quantified to high precision as a function of an applied external field. These data provide detailed information about a material's electronic structure, phase purity, and impurities, though interpreting this data can be challenging due to many contributing factors. In sub-single-domain particles of a magnetic material, an inherently time-dependent rotation of the entire particle spin becomes possible. This phenomenon, known as superparamagnetism (SPM), simultaneously represents a very early size-dependent property to be considered, while being one of the least explored in the current quantum materials era. This discrepancy is, at least in part, due to the need for models with less built-in complexity that can facilitate the generation of comparative data. In this work, we map an extensive dataset of variable-size SPM Fe3O4 (magnetite) to an intrinsic statistical model for their field-dependence. By constraining the SPM behavior to a probabilistic model, the data are apportioned to several decorrelated sources. From this, there is strong evidence that standard measures such as saturation magnetization, MS, are poor comparative parameters, being dependent on experimental knowledge and measurement of the magnetic mass. In contrast, parameters of the intrinsic probability distribution, such as the maximum susceptibility, χmax, are far better suited to describe the SPM behavior itself and do not propagate unknown magnetic mass error. By confining the data fitting to intrinsic variables of the model distribution, scaling parameters, and linear contributions, we find greater value in magnetic data, ultimately aiding potential synthesis diagnostics and prediction of new properties and functionality.

Size-Tunable Magnetite Nanoparticles from Well-Defined Iron Oleate Precursors.

The synthesis of iron oxide nanoparticles with control over size and shape has long been an area of research, with iron oleate being arguably the most successful precursor. Issues with reproducibility and versatility in iron oleate-based syntheses remain, however, in large part due to the mutable nature of its structure and stoichiometry. In this work, we characterize two new forms of iron oleate precursor that can be isolated in large quantities, show long-term stability, and have well-defined stoichiometry, leading to reproducible and predictable reactivity. Synthesis with these precursors is shown to produce iron oxide nanoparticles in a tunable size range of 4-16 nm with low size dispersity and properties consistent with magnetite in the superparamagnetic size regime.

A Size Threshold for Enhanced Magnetoresistance in Colloidally Prepared CoFe2O4 Nanoparticle Solids.

The phenomenon of granular magnetoresistance offers the promise of rapid functional materials discovery and high-sensitivity, low-cost sensing technology. Since its discovery over 25 years ago, a major challenge has been the preparation of solids composed of well-characterized, uniform, nanoscale magnetic domains. Rapid advances in colloidal nanochemistry now facilitate the study of more complex and finely controlled materials, enabling the rigorous exploration of the fundamental nature and maximal capabilities of this intriguing class of spintronic materials. We present the first study of size-dependence in granular magnetoresistance using colloidal nanoparticles. These data demonstrate a strongly nonlinear size-dependent magnetoresistance with smaller particles having strong ΔR/R ∼ 18% at 300 K and larger particles showing a 3-fold decline. Importantly, this indicates that CoFe2O4 can act as an effective room temperature granular magnetoresistor and that neither a high superparamagnetic blocking temperature nor a low overall resistance are determining factors in viable magnetoresistance values for sensing applications. These results demonstrate the promise of wider exploration of nontraditional granular structures composed of nanomaterials, molecule-based magnets, and metal-organic frameworks.