The recent revision of the International System of Units (SI)—which fixed the numerical values of nature’s fundamental constants—has opened new perspectives for practical realizations of SI units. Here we demonstrate an intrinsic resistance standard based on memristive nanoionic cells that operate in air at room temperature and are directly accessible to end users. By driving these devices into the quantum conductance regime and using an electrochemical-polishing-based programming strategy, we achieved quantum conductance levels that can be exploited as intrinsic standard values. An interlaboratory comparison confirmed metrological consistency, with deviations of –3.8% and 0.6% from the agreed SI values for the fundamental quantum of conductance, G ₀ , and 2 G ₀ , respectively. These results lay the groundwork for the implementation of national metrology institute services on chip and for the development of self-calibrating measurement systems with zero-chain traceability.

Nowadays, several types of infusion pump are commonly used for drug delivery, such as syringe pumps and peristaltic pumps. These instruments present different measuring features and capacities according to their use and therapeutic application. In order to ensure the metrological traceability of these flow and volume measuring equipment, it is necessary to use suitable calibration methods and standards. Two different calibration methods can be used to determine the flow error of infusion pumps. One is the gravimetric method, considered as a primary method, commonly used by National Metrology Institutes. The other calibration method, a secondary method, relies on an infusion device analyser (IDA) and is typically used by hospital maintenance offices. The suitability of the IDA calibration method was assessed by testing several infusion instruments at different flow rates using the gravimetric method. In addition, a measurement comparison between Portuguese Accredited Laboratories and hospital maintenance offices was performed under the coordination of the Portuguese Institute for Quality, the National Metrology Institute. The obtained results were directly related to the used calibration method and are presented in this paper. This work has been developed in the framework of the EURAMET projects EMRP MeDD and EMPIR 15SIP03.

The Portuguese Institute for Quality is responsible for the realization and dissemination of the frequency standard in Portugal. There are several techniques for frequency transfer, but we use a frequency mixer to detect phase variations between two light signals with different wavelengths, traveling along an optical fibre. In this paper, we present the development of a digital twin (DT) that replicates the use of a frequency mixer to improve the frequency transfer problem. A setup was built to train and validate the technique: a frequency mixer was used to determine the phase difference between the two signals, which are caused by temperature gradients in the fibre, together with real-time temperature data from sensors placed along the fibre and on the mixer itself. The DT was trained with two machine learning algorithms, in particular, ARIMA and LSTM networks. To estimate the accuracy of the frequency mixer working as a phasemeter, several sources of uncertainty were considered and included in the DT model, with the goal of obtaining a phase value measurement and its uncertainty in real time. The JCGM 100:2008 and JCGM 101:2008 approaches were used for the estimation of the uncertainty budget. With this work, we merge DT technology with a frequency mixer used for phase detection to provide its value and uncertainty in real time.

Digital transformation in metrology, essential for Industry 4.0 and digital quality infrastructure, is driving the development of digital twins (DTs) to improve accuracy, traceability, and automation. In this work, we developed and experimentally validated a DT at the Portuguese National Metrology Institute to monitor and optimize standard frequency transfer through an optical fibre link, with the aim of reducing phase instability induced by thermal disturbances, the main uncertainty source in such systems. The DT combines real-time sensor data, simulation, and machine learning algorithms to reproduce the physical link. A long short-term memory network predicts ambient temperature, which feeds a neural network to forecast phase drift. These predictions are used to calculate fractional frequency offsets and apply compensating corrections every 5 and 30 min. Measurement uncertainty from the physical and digital components, including the ML models, was quantified using Monte Carlo dropout to capture random and epistemic contributions. The global expanded uncertainty for phase measurements was estimated as 2.10 ps ( k = 2). The performance was evaluated using a special form of Allan deviation (ADEVS) and TIE_rms. Periodic corrections significantly improved link stability. At τ = 60 s, ADEVS decreased from 1.64 × 10 ⁻¹¹ s without correction to 4.48 × 10 ⁻¹² s with 5 min corrections and 4.94 × 10 ⁻¹² s with 30 min corrections. The 5 min scheme performed better at short integration times below 120 s, whereas the 30 min scheme gave lower ADEVS between τ = 240 s and τ = 1200 s. Up to 1200 s, both compensated signals improved by more than 50% compared with the uncorrected signal. This work demonstrates, for the first time, that a DT incorporating machine learning–based phase prediction and uncertainty quantification can act as a low-cost, algorithm-driven compensation mechanism for optical frequency transfer links used as secondary links, where primary-laboratory accuracy is not required.