Data acquisition and imaging using wavelet transform: a new path for high speed transient force microscopy

Head Exposure to Acceleration Database in Sport (HEADSport): a kinematic signal processing method to enable instrumented mouthguard (iMG) field-based inter-study comparisons

Objective

Instrumented mouthguard (iMG) systems use different signal processing approaches limiting field-based inter-study comparisons, especially when artefacts are present in the signal. The objective of this study was to assess the frequency content and characteristics of head kinematic signals from head impact reconstruction laboratory and field-based environments to develop an artefact attenuation filtering method (HEADSport filter method).

Methods

Laboratory impacts (n=72) on a test-dummy headform ranging from 25 to 150 g were conducted and 126 rugby union players were equipped with iMGs for 209 player-matches. Power spectral density (PSD) characteristics of the laboratory impacts and on-field head acceleration events (HAEs) (n=5694) such as the 95th percentile cumulative sum PSD frequency were used to develop the HEADSport method. The HEADSport filter method was compared with two other common filtering approaches (Butterworth-200Hz and CFC180 filter) through signal-to-noise ratio (SNR) and mixed linear effects models for laboratory and on-field events, respectively.

Results

The HEADSport filter method produced marginally higher SNR than the Butterworth-200Hz and CFC180 filter and on-field peak linear acceleration (PLA) and peak angular acceleration (PAA) values within the magnitude range tested in the laboratory. Median PLA and PAA (and outlier values) were higher for the CFC180 filter than the Butterworth-200Hz and HEADSport filter method (p<0.01).

Conclusion

The HEADSport filter method could enable iMG field-based inter-study comparisons and is openly available at https://github.com/GTBiomech/HEADSport-Filter-Method.

Unraveling Spatiotemporal Transient Dynamics at the Nanoscale via Wavelet Transform-Based Kelvin Probe Force Microscopy

Mechanistic probing of surface potential changes arising from dynamic charge transport is the key to understanding and engineering increasingly complex nanoscale materials and devices. Spatiotemporal averaging in conventional heterodyne detection-based Kelvin probe force microscopy (KPFM) inherently limits its time resolution, causing an irretrievable loss of transient response and higher-order harmonics. Addressing this, we report a wavelet transform (WT)-based methodology capable of quantifying the sub-ms charge dynamics and probing the elusive transient response. The feedback-free, open-loop wavelet transform KPFM (OL-WT-KPFM) technique harnesses the WT's ability to simultaneously extract spatial and temporal information from the photodetector signal to provide a dynamic mapping of surface potential, capacitance gradient, and dielectric constant at a temporal resolution 3 orders of magnitude higher than the lock-in time constant. We further demonstrate the method's applicability to explore the surface-photovoltage-induced sub-ms hole-diffusion transient in bismuth oxyiodide semiconductor. The OL-WT-KPFM concept is readily applicable to commercial systems and can provide the underlying basis for the real-time analysis of transient electronic and electrochemical properties.

Imaging beyond the surface region: Probing hidden materials via atomic force microscopy

Probing material properties at surfaces down to the single-particle scale of atoms and molecules has been achieved, but high-resolution subsurface imaging remains a nanometrology challenge due to electromagnetic and acoustic dispersion and diffraction. The atomically sharp probe used in scanning probe microscopy (SPM) has broken these limits at surfaces. Subsurface imaging is possible under certain physical, chemical, electrical, and thermal gradients present in the material. Of all the SPM techniques, atomic force microscopy has entertained unique opportunities for nondestructive and label-free measurements. Here, we explore the physics of the subsurface imaging problem and the emerging solutions that offer exceptional potential for visualization. We discuss materials science, electronics, biology, polymer and composite sciences, and emerging quantum sensing and quantum bio-imaging applications. The perspectives and prospects of subsurface techniques are presented to stimulate further work toward enabling noninvasive high spatial and spectral resolution investigation of materials including meta- and quantum materials.

Navigating with chemometrics and machine learning in chemistry

Chemometrics and machine learning are artificial intelligence-based methods stirring a transformative change in chemistry. Organic synthesis, drug discovery and analytical techniques are incorporating machine learning techniques at an accelerated pace. However, machine-assisted chemistry faces challenges while solving critical problems in chemistry due to complex relationships in data sets. Even with increasing publishing volumes on machine learning, its application in areas of chemistry is not a straightforward endeavour. A particular concern in applying machine learning in chemistry is data availability and reproducibility. The present review article discusses the various chemometric methods, expert systems, and machine learning techniques developed for solving problems of organic synthesis and drug discovery with selected examples. Further, a concise discussion on chemometrics and ML deployed in analytical techniques such as, spectroscopy, microscopy and chromatography are presented. Finally, the review reflects the challenges, opportunities and future perspectives on machine learning and automation in chemistry. The review concludes by pondering on some tough questions on applying machine learning and their possibility of navigation in the different terrains of chemistry.

Development of a flexure-based nano-actuator for high-frequency high-resolution directional sensing with atomic force microscopy

Scanning probe microscopies typically rely on the high-precision positioning of a nanoscale probe in order to gain local information about the properties of a sample. At a given location, the probe is used to interrogate a minute region of the sample, often relying on dynamical sensing for improved accuracy. This is the case for most force-based measurements in atomic force microscopy (AFM) where sensing occurs with a tip oscillating vertically, typically in the kHz to MHz frequency regime. While this approach is ideal for many applications, restricting dynamical sensing to only one direction (vertical) can become a serious limitation when aiming to quantify the properties of inherently three-dimensional systems, such as a liquid near a wall. Here, we present the design, fabrication, and calibration of a miniature high-speed scanner able to apply controlled fast and directional in-plane vibrations with sub-nanometer precision. The scanner has a resonance frequency of ∼35 kHz and is used in conjunction with a traditional AFM to augment the measurement capabilities. We illustrate its capabilities at a solid-liquid interface where we use it to quantify the preferred lateral flow direction of the liquid around every sample location. The AFM can simultaneously acquire high-resolution images of the interface, which can be superimposed with the directional measurements. Examples of sub-nanometer measurements conducted with the new scanner are also presented.