Abstract Accurate quantitative analysis of homogeneous bulk samples is critical in materials science and mineral industry applications, yet conventional standards‐based Energy Dispersive Spectroscopy (EDS) quantitative analysis remains reliable and widely used. However, for some laboratories or sample types, obtaining, maintaining, or matching appropriate physical standards may be inconvenient or impractical. In this study, we introduce a dynamic framework for elemental quantification of homogeneous multi‐element bulk samples investigated in an SEM at an accelerating voltage of 20 kV by integrating Monte Carlo X‐ray (MC X‐ray), a Monte Carlo simulation program, with artificial neural networks (ANNs). Synthetic spectra and corresponding weight fraction pairs were generated using MC X‐ray under the same 20 kV measurement condition, and a multilayer perceptron (MLP) was trained to map spectral features to elemental weight fractions. Validation across 11 certified standard samples yielded root mean square error (RMSE) values from 0.0056 to 0.0179 in weight fraction and coefficients of determination ( R 2 ) from 0.9873 to 0.9981, even in cases with overlapping peaks. For elements with certified weight fractions of at least 0.05, the mean absolute percentage error was 4.41%. The model, tailored to specific sample and measurement conditions, enables quantitative analysis in under 15 min on consumer‐grade hardware. By reducing the dependence on physical standards and calibration procedures, this approach provides a complementary simulation‐based method for quantitative microanalysis when suitable measured standards or stored standard libraries are not readily available for the analysis of homogeneous bulk materials.
Abstract Collagen hydrogels are widely used as biomimetic three‐dimensional matrices whose mechanical behaviour depends strongly on fibrillar micro‐ and nanoarchitecture and crosslinking state. Electron microscopy offers high spatial resolution for structural analysis, but sample preparation can substantially distort highly hydrated collagen networks, thereby confounding interpretation. Here, we compared common SEM and TEM preparation workflows for their ability to preserve structurally subtle, mechanically validated differences in collagen type I hydrogels. Collagen hydrogels containing riboflavin were photochemically crosslinked by blue‐light exposure, and the resulting increase in bulk stiffness was first confirmed by rotational rheometry. Samples were then prepared using various workflows for SEM and TEM analysis based on classical glutaraldehyde fixation or high‐pressure freezing. As control experiments, we performed nitrogen plunge freezing to visualise the effect of ice crystal formation, and high‐pressure freezing with ethanol cryoprotection to rule out the influence of ice crystal artefacts. The results show that sample preparation‐induced artefacts strongly influence structural interpretation in electron microscopy of collagen hydrogels. The methodology based on glutaraldehyde fixation altered network morphology, while the nitrogen plunge freezing based process introduced severe freezing artefacts attributed to ice crystal formation, obscuring the subtle pore‐size changes associated with riboflavin/blue‐light crosslinking. The high‐pressure freezing based process with and without cryoprotectant retained the ability to resolve crosslinking‐associated structural differences in these soft collagen hydrogels. This indicates that high‐pressure frozen samples of the collagen hydrogels used in this study are already free of visible ice crystal damage and that the use of additional cryoprotectants with unknown structural effects is unnecessary. More broadly, the study supports combining mechanical validation with electron microscopy when assessing soft hydrogel architectures to minimise the potential to interpret preparation‐based artefacts as structural features.
Abstract Since its introduction 40 years ago, atomic force microscopy (AFM) has transformed nanoscale imaging across the physical and life sciences. In biology, AFM has provided unprecedented insights into nanoscale biomolecular organisation under near‐physiological conditions, revealing structures and interactions inaccessible to many conventional approaches. However, biological function is inherently dynamic, and understanding molecular mechanisms requires moving beyond static structural snapshots towards direct observation of processes across multiple spatial and temporal scales. In this review, we discuss emerging approaches that are extending the capabilities of AFM beyond traditional imaging, pushing the limits of temporal resolution, spatial precision, quantitative measurement, and data analysis. We highlight advances in speed that enable direct visualisation of molecular dynamics, approaches for sub‐millisecond measurements, methods that overcome conventional resolution limits, and computational approaches that enhance image analysis, automation, and information extraction from complex datasets. Together, these developments are transforming AFM from a technique focused primarily on high‐resolution structural imaging into a platform for dynamic, quantitative, and high‐throughput investigation of biological systems. Finally, we consider how these advances may shape the next 40 years of AFM, enable autonomous measurements and reveal biological mechanisms across previously inaccessible spatial and temporal scales.
