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The vexing error of excess variance in the sizing of single particles degrades accuracy in applications ranging from quality control of nanoparticle products to hazard assessment of nanoplastic byproducts. The particular importance of lipid nanoparticles for vaccine and medicine delivery motivates this comment on a publication$^{\textrm{1}}$ in ACS Nano. In ref 1, the benchmark measurements of a nanoparticle standard manifest large errors of the size distribution that contradict the claim of validation. Such errors can bias the correlation of fluorescence intensity as an optical proxy for the molecular loading of lipid nanoparticles and give misleading insights from power-law models of intensity$-$size data. Looking forward, measurement error models have the potential to address this widespread issue., Comment: Peer reviewed and pending acceptance by ACS Nano

Plastic nanoparticles present technological opportunities and environmental concerns, but measurement challenges impede product development and hazard assessment. To meet these challenges, we advance a lateral nanoflow assay that integrates complex nanofluidic replicas, optical localization microscopy, and novel statistical analyses. We apply our sample-in-answer-out system to measure polystyrene nanoparticles that sorb and carry hydrophobic fluorophores. An elegant scaling of surface forces automates advection and dominates diffusion to drive the analytical separation of colloidal nanoparticles by their steric diameters. Reference nanoparticles, with a mean of 99 nm and a standard deviation of 8.4 nm, test the unknown limits of silicone replicas to function as separation matrices. New calibrations correct aberrations from microscope and device, improving the accuracy of reducing single micrographs to joint histograms of steric diameter and fluorescence intensity. A dimensional model approaches the information limit of the system to discriminate size exclusion from surface adsorption, yielding errors of the mean ranging from 0.2 nm to 2.3 nm and errors of the standard deviation ranging from 2.2 nm to 4.2 nm. A hierarchical model accounts for metrological, optical, and dimensional variability to reveal a fundamental structure-property relationship. Intensity scales with diameter to the power of 3.6 +/- 0.5 at 95 % coverage, confounding basic concepts of surface adsorption or volume absorption. Distributions of fluorescivity - the product of the number density, absorption cross section, and quantum yield of an ensemble of fluorophores - are ultrabroad and asymmetric, limiting any inference from fluorescence intensity. This surprising characterization of common nanoplastics resets expectations for optimizing products, applying standards, and understanding byproducts.

Focused-ion-beam machining is a powerful process to fabricate complex nanostructures, often through a sacrificial mask that enables milling beyond the resolution limit of the ion beam. However, current understanding of this super-resolution effect is empirical in the spatial domain and nonexistent in the temporal domain. This article reports the primary study of this fundamental tradespace of resolution and throughput. Chromia functions well as a masking material due to its smooth, uniform, and amorphous structure. An efficient method of in-line metrology enables characterization of ion-beam focus by scanning electron microscopy. Fabrication and characterization of complex test-structures through chromia and into silica probe the response of the bilayer to a focused beam of gallium cations, demonstrating super-resolution factors of up to 6 $\pm$ 2 and improvements to volume throughput of at least factors of 42 $\pm$ 2, with uncertainties denoting 95 % coverage intervals. Tractable theory models the essential aspects of the super-resolution effect for various nanostructures. Application of the new tradespace increases the volume throughput of machining Fresnel lenses by a factor of 75, which we introduce as projection standards for optical microscopy. These results enable paradigm shifts of sacrificial masking from empirical to engineering design, and from prototyping to manufacturing.