Manual digital timing devices such as stopwatches are ubiquitous in the education sector for experimental work where automated electronic timing is unavailable or impractical. The disadvantage of manual timing is that the experimenter introduces an additional systematic error and random uncertainty to a measurement that hitherto could only be approximated and which masks useful information on uncertainty due to variations in the physical conditions of the experiment. A model for the reaction time of a timekeeper using a stopwatch for a single anticipated visual stimulus of the type encountered in physics experiments is obtained from a set of 4304 reaction times from timekeepers at swimming competitions. The reaction time is found to be well modelled by the normal distribution N(ϵ, σ²) = N(0.11, 0.07²) in units of seconds where ϵ and σ² are the systematic error and variance for a single time measurement. Consistency between timekeepers is shown to be very good. The reaction time for a stopwatch-operated start and stop experiment can therefore be modelled by N(0, 0.10²), assuming that the average reaction time is the same in both cases. This makes a significant contribution to the uncertainty of most manually timed measurements. This timing uncertainty can be subtracted out of the variation observed in repeat measurements in the real experiment to reveal the uncertainty solely associated with fluctuations in the physical conditions of the experiment.

Nuclear magnetic resonance (NMR) relaxation experimentation is an effective technique for probing the dynamics of proton spins in porous media, but interpretation requires the application of appropriate spin-diffusion models. Molecular dynamics (MD) simulations of porous silicate-based systems containing a quasi-two-dimensional water-filled pore are presented. The MD simulations suggest that the residency time of the water on the pore surface is in the range 0.03–12 ns, typically 2–5 orders of magnitude less than values determined from fits to experimental NMR measurements using the established surface-layer (SL) diffusion models of Korb and co-workers [ Phys. Rev. E 56, 1934 (1997)]. Instead, MD identifies four distinct water layers in a tobermorite-based pore containing surface ${\mathrm{Ca}}^{2+}$ ions. Three highly structured water layers exist within 1 nm of the surface and the central region of the pore contains a homogeneous region of bulklike water. These regions are referred to as layer 1 and 2 (L1, L2), transition layer (TL), and bulk (B), respectively. Guided by the MD simulations, a two-layer (2L) spin-diffusion NMR relaxation model is proposed comprising two two-dimensional layers of slow- and fast-moving water associated with L2 and layers TL+B, respectively. The 2L model provides an improved fit to NMR relaxation times obtained from cementitious material compared to the SL model, yields diffusion correlation times in the range 18–75 ns and 28–40 ps in good agreement with MD, and resolves the surface residency time discrepancy. The 2L model, coupled with NMR relaxation experimentation, provides a simple yet powerful method of characterizing the dynamical properties of proton-bearing porous silicate-based systems such as porous glasses, cementitious materials, and oil-bearing rocks.

Fast-field-cycling nuclear magnetic resonance (FFC-NMR) is a powerful technique for non-destructively probing the properties of fluids contained within the pores of porous materials. FFC-NMR measures the spin–lattice relaxation rate $R_1\left(f\right)$ as a function of NMR frequency f over the kHz to MHz range. The shape and magnitude of the $R_1\left(f\right)$ dispersion curve is exquisitely sensitive to the relative motion of pairs of spins over time scales of picoseconds to microseconds. To extract information on the nano-scale dynamics of spins, it is necessary to identify a model that describes the relative motion of pairs of spins, to translate the model dynamics to a prediction of $R_1\left(f\right)$ and then to fit to the experimental dispersion. The principles underpinning one such model, the 3 $\tau$ model, are described here. We present a new fitting package using the 3 $\tau$ model, called 3TM, that allows users to achieve excellent fits to experimental relaxation rates over the full frequency range to yield five material properties and much additional derived information. 3TM is demonstrated on historic data for mortar and plaster paste samples.

The hydration process of Portland cement is still not completely understood. For instance, it is not clear what produces the induction period, which follows the initial period of fast reaction, and is characterized by a reduced reactivity. To contribute to such understanding, we compare here the hydration process of two cement samples, the simple cement paste and the cement paste containing calcium nitrate as an accelerator. The hydration of these samples is monitored during the induction period using two different low-field nuclear magnetic resonance (NMR) relaxometry techniques. The transverse relaxation measurements of the ¹H nuclei at 20 MHz resonance frequency show that the capillary pore water is not consumed during the induction period and that this stage is shortened in the presence of calcium nitrate. The longitudinal relaxation measurements, performed at variable Larmor frequency of the ¹H nuclei, reveal a continuous increase in the surface-to-volume ratio of the capillary pores, even during the induction period, and this increase is faster in the presence of calcium nitrate. The desorption time of water molecules from the surface was also evaluated, and it increases in the presence of calcium nitrate.

Nuclear magnetic resonance (NMR) relaxometry has evolved from early theoretical insights into a dynamic and versatile analytical technique capable of probing molecular and ionic motion across diverse fields. Rooted in the foundational work by many different scientists (e.g., Bloch, Purcell, Torrey, Hahn, Bloembergen, Pound, and Solomon, just to name a few), relaxometry has progressed through pivotal advancements such as Redfield's theory and the development of time‐domain (TD) and fast field‐cycling (FFC) methodologies. While the former enables rapid, low‐cost analysis of relaxation time distributions, widely applied in soft matter and quality control, the latter provides frequency‐resolved nuclear magnetic resonance dispersion (NMRD) profiles that capture dynamic processes across multiple timescales, revealing deeper insights into molecular interactions in heterogeneous systems. Recent innovations in instrumentation have expanded the applicability of relaxometry. Moreover, its integration with modalities such as diffusimetry and imaging has opened new routes for spatially resolved and multimodal analyses. Applications now span materials science, biomedicine, and environmental studies. In polymers and porous media, relaxometry reveals segmental dynamics and surface interactions; in biological tissues, NMRD profiles differentiate healthy from pathological states, offering diagnostic potential. Emerging applications include contrast agent development, soil hydration analysis, microplastic detection, and wastewater monitoring. This paper offers a comprehensive overview of the field's historical trajectory, methodological advancements, and expanding application landscape. Emphasis is placed on the synergy between TD and FFC‐NMR approaches and the ongoing transition toward portable, real‐time, and multimodal relaxometric systems. NMR relaxometry is poised to become a mainstream tool in diagnostics, materials characterization, and environmental monitoring.