National Physical Laboratory

BackgroundIndependent verification of the manufacturer‐provided dosimetry data for 106Ru ophthalmic brachytherapy applicators is crucial for safe and accurate treatment, yet a standardized, traceable method for clinical absolute dosimetry has been lacking.PurposeThis work establishes a complete framework for traceable absorbed dose to water measurements of 106Ru eye plaques in absolute units of Gray, complementing the high‐precision BetaCheck‐106™ setup with a robust detector calibration methodology independent of the source manufacturer.MethodsThree microSilicon diode detectors were calibrated in traceable 60Co and 6 MeV electron reference beams. Depth‐dose measurements for four CCB‐type 106Ru plaques were performed in water using the BetaCheck‐106™ setup. Monte Carlo (MC) simulations were employed to calculate depth‐dependent beam quality correction factors kQ,Q0 which account for the detector's response in the 106Ru field relative to the calibration beams. The method was validated against measurements performed at the National Physical Laboratory (NPL) using alanine dosimetry. MC simulations were also used to investigate the water‐equivalence of the NPL alanine/PMMA phantom setup.ResultsThe MC‐calculated correction factors for the diodes showed a significant depth‐dependence, underscoring the necessity of such corrections in the steep 106Ru depth‐dose gradient. The dose rates determined with the calibrated diodes were in agreement with the NPL alanine results. Both methods yielded dose rates systematically lower than those provided in the manufacturer's certificates, though generally within the stated uncertainties. The MC simulations revealed substantial non‐water equivalence correction factors for the alanine/PMMA phantom, highlighting the advantage of direct measurements in water.ConclusionsWe present a novel, comprehensive methodology for independent and traceable absolute dosimetry of 106Ru applicators. By combining a dedicated water phantom setup with diode detectors calibrated against external beam standards and MC‐derived correction factors, this framework empowers clinical users to perform robust verification measurements, filling a critical gap in the quality assurance of ocular brachytherapy.

Global burned area (BA) products are commonly available at a Non-Time Critical (NTC) basis, several months or even several years from the present date; i.e. they are unavailable for Near-Real Time (NRT) applications. The Copernicus Land Monitoring Service (CLMS) delivers the only global BA product in NRT, since recently, at very high accuracy, comparable to the most accurate non-CLMS NTC product (FIRECCIS311). However, global BA products are generated from coarse >= 300 m reflectance observations. Despite the Sentinel-2 mission having been in operation since 2017, providing decadal resolution 10-50 m reflectance data every ~5 days, and despite the well-known benefits of using decadal resolution data to estimate BA, a global Sentinel-2 NRT BA algorithm does not exist. The purpose of this study is to adapt and apply the latest developments in NRT detection, as implemented in the CLMS, to Sentinel-2 L2A imagery. The mapping method uses a neural network (NN) with 2D convolutional layers, followed by a Long Short-Term Memory (LSTM) layer. The NN processes the time series of reflectance images on a per-pixel basis, with convolutional layers applied along the spectral and temporal dimensions. The time series of fractional BA maps, predicted by the NN, are combined with time series of spatio-temporal density of VIIRS active fire detections. Such a combination consists of a logistic model and allows the reduction of false positives (such as cloud shadows). The NN is trained on a sample dataset automatically generated from time series reflectance observations (Sentinel-2 data in this case), extracted over locations of VIIRS active fire detections across the Globe for the year 2020, and corresponding estimates of fractional BA, derived from physically-based radiative transfer modelling. The mapping method generates one BA map for each new Sentinel-2 image available (referred to as BAS2nrt0), which is updated with images from the following 5 days (referred to as BAS2nrt5) and the following 10 days (referred to as BAS2nrt10). The additional images available after the mapping day are expected to reduce false positives due to cloud shadows. The mapping method also generates an NTC BA map for each calendar month (referred to as BAS2ntc), with images available for a buffer of 45 days around the month. The algorithm results are validated against an independent global reference dataset for the year 2019, which includes long time series of Landsat-derived BA maps covering 105 sampling units distributed across the Globe. The analysis of the 2019 validation results shows that the accuracy of the proposed Sentinel-2 products is high regardless of estimation timeliness. As expected, (1) the accuracy of the NTC product, Dice coefficient (DC) of 87.2%, is higher than the NRT products, DC 82.7–85.4%, and (2) the accuracy of the NRT product is increased with each update. Such accuracy levels are remarkably high: the accuracy of NRT estimates is comparable to a precedent global non-CLMS NTC Sentinel-2 BA mapping (DC 81.8%).

ESG (Environmental, Social, Governance) reporting is essential for industry as it helps secure investment for companies’ development. While Scope 1 are direct emissions and Scope 2 are indirect emissions, most of the industrial players report Scope 2 emissions from the use of energy (electricity and gas): these are carbon emissions that are emitted in the power station that uses fossil fuels (oil, coal, gas, biomass, etc.), see [1].Conventional way to report company’s carbon emissions of Scope 2 is to obtain electrical meter readings and multiply them by the average carbon intensity of the electric grid that supplies electricity. In the UK, such carbon factors were previously published (annually) by the Department for Environment, Food, and Rural Affairs (Defra), then more recently by the Department for Energy Security and Net Zero (DESNZ). These average annual factors are approximate, and actual fuel mix of the electrical grid varies within a few minutes, depending on the operating power generators.In some cases, the annual carbon intensity may underestimate the actual intensity of the grid. This usually happens in Europe in winter, when a large number of gas-fuelled generators are active to provide sufficient heating, and at the same time wind conditions are placid, providing little of renewable energy. In other cases, when there is lots of wind-generated energy and less gas-generated energy (for example, on a windy summer day), the average carbon factor may overestimate actual carbon intensity of the grid.In several case studies, we demonstrate that such discrepancies may reach 10-15% of the total carbon emissions, as they are presented in quarterly or annual ESG reports. The results suggest that the current way of reporting carbon emissions should be revised, so that actual state of the dynamical energy grid would be taken into account for improvement of ESG reporting. Subsequently, this will impact their ESG standing and potential investment, which is crucial for European business as well as for the correct accounting of the impact of European carbon emissions [2].

There has been explosive growth in the field of remote sensing of methane plume from aircraft and satellite. The global reach and inherent spatial sampling capabilities of on-orbit instruments make them uniquely suited for consistent, repeatable surveys across regions and borders. These measurements have primarily been applied to the fossil energy and waste sectors (Cusworth et al., 2022; Thorpe et al., 2023), with current satellites typically detecting emissions exceeding ≈100 kg CH₄ per hour, while airborne platforms can observe sources as small as ≈10 kg CH₄ per hour.Despite the rapid growth in observational capacity, challenges remain. Divergent emissions estimates, opaque methodologies, and inconsistent validation approaches can erode confidence in remote sensing-based emissions data. The emergence of non-public-sector missions using proprietary methods—often without full transparency across the data chain—further highlights the need for community-accepted practices to ensure traceability, comparability, and scientific credibility.To address this need, the greenhouse gas (GHG) community—through the Committee on Earth Observation Satellites (CEOS) and National Metrology Institutes (NMIs)— developed a document in 2025 to articulate commonly accepted approaches for quantifying methane emissions based on observed plumes (Worden et al., 2025). It provides guidance spanning from Level 0/1 radiance, to Level 2 concentration, to Level 4 emissions, and includes current practices for validation and quality assessment. The focus is on emissions derived from discrete plumes, rather than from spatially diffuse sources. In this poster, we will discuss some key points of the current practices report as well as plans for next steps to perform intercomparisons and work towards a Best Practices document. This work has shifted from NIST to the Climate Data Collaborative of the Data Foundation in the US, and will be performed in collaboration with researchers and agencies across the US and Europe in 2026 including CEOS, CGMS, NPL, LLBL, UKSA, ESA, and the MEDUSA project.

Fibre-optic sensing technology is transforming seafloor monitoring by enabling dense, continuous measurements across vast distances using existing telecommunication infrastructure. Distributed acoustic sensing (DAS) and optical interferometry [1] have demonstrated remarkable potential for earthquake detection, ocean dynamics monitoring, and hazard early warning. However, for these technologies to be used for these applications, the transfer function between environmental perturbations and measured optical signal changes in submarine cables needs to be known.We present the, to the best of our knowledge, first controlled-environment characterisation of submarine cable responses to active seismic and acoustic sources, comparing DAS and optical interferometry measurements with ground-truth data from 58 geophones, 20 three-component seismometers, and microphones [2]. Our results reveal three key findings:In contrast with proposed theoretical models [3], our interferometric measurements show first-order sensitivity to broadside seismic sources, enabling localisation of arrivals along straight fibre links.We identify a previously unreported fast-wave phenomenon, attributed to seismic energy coupling into the cable's metal armour and propagating at velocities exceeding 3.5 km/s, significantly altering recorded waveforms.We compared measurements between adjacent fibres within the same cable. Results show significant discrepancies between the measured waveforms, which should be considered in applications operating in a similar frequency range as our tests.These findings show the complexity of submarine cable mechanics and their impact on optical sensing performance. Understanding these processes is critical for calibrating transfer functions and improving the reliability of fibre-based geophysical observations. In addition to these findings, we also discuss the limitations of our methodology, which primarily arise from the limited range of seismic source frequencies available. Our work presents a first step towards understanding the complex transfer function of environmental perturbations to optical signals in subsea cables, advancing the vision of large-scale, cost-effective Earth observation systems.[1] Marra, G. et al. Optical interferometry–based array of seafloor environmental sensors using a transoceanic submarine cable. Science 376 (6595), 874–879 (2022)[2] Fairweather, D.M., Tamussino, M., Masoudi, A. et al. Characterisation of the optical response to seismic waves of submarine telecommunications cables with distributed and integrated fibre-optic sensing. Sci Rep 14, 31843 (2024)[3] Fichtner, A., Bogris, A., Nikas, T. et al. Theory of phase transmission fibre-optic deformation sensing. Geophysical Journal International, 231(2), 1031–1039, (2022)

Satellite data below 100 m resolution can be of great benefit for prevention of geohazards. To utilise spatial and temporal data efficiently, it is necessary to develop data science techniques that are sensitive, computationally light, and capable of revealing signatures of critical events in bulky multivariate data. This emphasis on computationally light yet physically grounded detection aligns with recent climate emulation work that motivates efficient data-driven pipelines for extracting dynamical signatures from large observational datasets [1]. We apply tipping point analysis e.g., Early Warning Indicators (EWS), adapted to multivariate data flows, to demonstrate how this methodology can help complement and augment field work in the peatlands, thus optimising resources.EWS are based on structural changes in trajectories of dynamical systems, which are described by autocorrelations and variability of the system potential [2-4]. Conventionally, peatlands are studied using expensive and slow ground surveys, but we show that equivalent information can be derived from the satellite Interferometric Synthetic Aperture Radar (InSAR) 6-12 day surface motion data using tipping point analysis. This includes processing the order of hundreds of thousands of time series potentially over 100’s of km, in combination with GIS data provided by stakeholders.We demonstrate a case study using InSAR surface motion data over ~400km2area in Scotland with areas of critical changes in the soil surface. In a blind test, the area of a large fire (60km2) in Scottish peatlands was identified and its detection coincided with the area of actual fire damage in comparison with ground observations and existing fire detection tools based on MODIS data. This approach is promising and may be developed further to better understand peatland behaviour before and after such extreme events.

Agricultural methane emissions, from enteric fermentation and manure management, account for approximately 32% of global anthropogenic methane emissions, yet standardized farm- or herd-scale quantification methods remain lacking (Nisbet et al., 2025). Downwind mobile surveys using Gaussian plume modelling for point sources, primarily from oil and gas, and tracer dispersion methods for diffused sources, such as landfills, have been demonstrated to be effective, relatively low-uncertainty approaches for quantifying methane emissions.Here, we present a computationally efficient framework for quantifying farm-scale methane emissions using Gaussian plume modelling, developed for small- to medium- scale farms in the UK, with ongoing work to extend the approach to grazing cattle. Using a Lagrangian particle model (Oettl & Kuntner, 2024), a virtual upwind point source is assigned to encompass the farm emission footprint (De Visscher, 2014). Gaussian dispersion modelling with Monte Carlo iterations is then applied to downwind vehicle-based methane measurements to derive farm-scale emission estimates.These estimates are evaluated against results from a simultaneous controlled tracer release conducted by the National Physical Laboratory at the same farm site. We discuss associated uncertainties and highlight future improvements, including process automation, source apportionment, and methods for quantifying emissions from grazing herds.

Dissipative solitons and their frequency combs hold great potential for applications in optical communications, spectroscopy, precision time-keeping and beyond. Recent demonstrations based on the combination of second-harmonic generation and degenerate optical parametric oscillators (OPOs) show the interest in shifting soliton spectra away from the telecom's C-band pump sources. However, these approaches lack the tunability offered by nondegenerate OPOs. This work presents a proof-of-principle demonstration of solitons in a nondegenerate OPO system based on a silicon-nitride microresonator, with engineered dispersion and optimised coupling rates. By pumping a relatively low-Q resonance in the C-band, we excite a signal soliton comb centred around a far-detuned, high-Q, O-band resonance, as well as repetition-rate-locked combs at the pump and idler frequencies, with the latter occurring at a wavelength beyond 2 μm. The solitons supported by this platform - hyperparametric solitons - are distinct from other families of dissipative solitons, as they emerge when the narrow-band signal mode, phase-matched under negative pump detuning, reaches sufficient power to drive bistability in the parametric signal. We investigate the properties of hyperparametric solitons, including their parametrically generated background and multisoliton states, both experimentally and through theoretical modelling.

Abstract Classical thermoelectric (TE) phenomena require a junction between two dissimilar materials with different Seebeck coefficients to either generate current via temperature differences at the junctions (Seebeck effect) or to cool/heat these junctions by applying external current (Peltier effect). While modifying the Seebeck coefficient via material composition and heterostructuring using different materials is well known, changing the Seebeck coefficient of a uniform material just by geometrical patterning was neither known, nor foreseen. Here, we report, for the first time, the ability to engineer the Seebeck coefficient, the dominant parameter of TE performance, in an arbitrary large area of a single uniform two-dimensional (2D) material by geometrical patterning. The Seebeck coefficient modification decays in an exponential manner, over a characteristic “decay length”, dTE ~ 1 μm, that we link to the product of Fermi velocity of the charge carriers and the time of phonon-electron energy exchange in a 2D material. By constructing nanopatterns of voids with pitch smaller than dTE, we effectively create TE “junctions”, opening up novel avenues for thermal management in nanoelectronics, photodetector arrays and TE energy generation.