Experimental Calibration Curves Research Articles

Improved determination of protein turnover rate with heavy water labeling by mass isotopomer ratio selection.

We describe a reverse lookup method to calculate the molar fraction of new synthesis from numerically approximated peptide isotopomer profiles in heavy water labeling mass spectrometry experiments. Using an experimental calibration curve comprising mixtures of fully unlabeled and fully labeled proteomes at various proportions, we show that this method provides a straightforward way to calculate the proportion of new proteins in a protein pool from arbitrarily chosen isotopomer ratios. We next analyzed which of the isotopomer pairs within the peptide isotope envelope yielded isotopomer time courses that fit most closely to kinetic models, and found that the identity of the isotopomer pair depends partially on the number of deuterium accessible labeling sites of the peptide. We next derived a strategy to automatically select the isotopomer pairs to calculate turnover rates based on peptide sequence, and showed that this increases the coverage of existing proteome-wide turnover experiments in multiple data sets of the mouse heart, liver, kidney, and skeletal muscle by up to 25-82%.

Aqueous size exclusion chromatography applied to polymer analysis: experimental conditions and molecular weight calibration curves

Aqueous mode size exclusion chromatography (SEC) was employed for the analysis and construction of molecular weight (MW) calibration curves of three water-soluble polymers, namely, polyethylene glycol, polyethylene oxide, and polyacrylic acid sodium salt. Several calibration curves were obtained, varying chromatographic conditions such as columns arrangement, ionic strength, temperature and pH, in addition trends in polymeric chromatographic behavior were examined. The variation in SEC distribution coefficients at different temperatures was found to be below 10 %, indicating that the studied polymers follow an ideal SEC mechanism under the tested conditions. Thus, differences in chromatographic behavior were ascribed to changes in polymer configuration induced by media and/or temperature. These variations in morphology were consistent with the observed SEC behavior.Regarding MW calibration, polynomial regression models ranging from first to fifth order were applied, and the most adequate ones were selected based on their fit and prediction capabilities. Third order polynomials were the preferred models for polyethylene glycol and polyacrylic acid sodium salt, independently of chromatographic conditions. Meanwhile for polyethylene oxide, either third or fifth-order polynomial models were optimal depending on the chromatographic conditions. All the selected regression models presented coefficients of multiple determination (R2) above 0.990, while achieving relative errors of prediction (REP%) in MW ranging from 0.3 to 4 % for cross-validation.

Chemiresistors Based on Hybrid Nanostructures Obtained from Graphene and Conducting Polymers with Potential Use in Breath Methane Detection Associated with Irritable Bowel Syndrome.

This paper describes the process of producing chemiresistors based on hybrid nanostructures obtained from graphene and conducting polymers. The technology of graphene presumed the following: dispersion and support stabilization based on the chemical vapor deposition technique; transfer of the graphene to the substrate by spin-coating of polymethyl methacrylate; and thermal treatment and electrochemical delamination. For the process at T = 950 °C, a better settlement of the grains was noticed, with the formation of layers predominantly characterized by peaks and not by depressions. The technology for obtaining hybrid nanostructures from graphene and conducting polymers was drop-casting, with solutions of Poly(3-hexylthiophene (P3HT) and Poly[(9,9-dioctylfluorenyl-2,7-diyl)-co-bithiophene] (F8T2). In the case of F8T2, compared to P3HT, a 10 times larger dimension of grain size and about 7 times larger distances between the peak clusters were noticed. To generate chemiresistors from graphene-polymer structures, an ink-jet printer was used, and the metallization was made with commercial copper ink for printed electronics, leading to a structure of a resistor with an active surface of about 1 cm2. Experimental calibration curves were plotted for both sensing structures, for a domain of CH4 of up to 1000 ppm concentration in air. A linearity of the curve for the low concentration of CH4 was noticed for the graphene structure with F8T2, presenting a sensitivity of about 6 times higher compared with the graphene structure with P3HT, which makes the sensing structure of graphene with F8T2 more feasible and reliable for the medical application of irritable bowel syndrome evaluation.

Real-time artificial intelligence enhanced defect engineering in CeO2 nanostructures

CeO2 nanostructures have been utilized for various biomedical, sensor, and catalysis applications owing to their unique defect structure, enabling them to have regenerative oxidative properties. Defect engineering in CeO2 nanostructures has major importance, enabling them to be utilized for specific applications. Despite various synthesis methods, it is challenging to have precise and reversible control over defect structures. Against this backdrop, in the current work, we have explored machine learning (ML) enhanced defect engineering of CeO2 nanofilms. In our earlier work [J. Vac. Sci. Technol. A 39, 060405 (2021)], we have developed an atomic layer deposition process for CeO2 using in situ ellipsometry measurements. In the current work, data collected through in situ spectroscopic ellipsometry and ex situ XPS have been correlated using two ML algorithms (gradient boost and random forest regressor) to exert better control over the chemical properties. Defect structures are one of the desired properties in CeO2 nanomaterials, characterized by the Ce3+/Ce4+ oxidation state ratio leading to its regenerative properties. We have shown that the defect structure of the CeO2 nanofilms can be predicted using in situ ellipsometry data in real time using a trained ML algorithm using two different methods. The first method involves an indirect approach of thickness prediction using an ML algorithm (k-nearest neighbors) followed by Ce3+/Ce4+ estimation using an experimental calibration curve. The second method with a more direct approach involves Ce3+/Ce4+ prediction using real-time ellipsometry data (amplitude ratio ψ and phase difference Δ) using gradient boost and random forest regressor.

A Developed Jerk Sensor for Seismic Vibration Measurements: Modeling, Simulation and Experimental Verification

Acceleration-based sensors are widely used in indicating the severity of damage caused to structural buildings during dynamic events. The force rate of change is of interest when investigating the effect of seismic waves on structural elements, and hence the calculation of the jerk is necessary. For most sensors, the technique used for measuring the jerk (m/s3) is based on differentiating the time–acceleration signal. However, this technique is prone to errors especially in small amplitude and low frequency signals, and is deemed not suitable when online feedback is required. Here, we show that direct measurement of the jerk can be achieved using a metal cantilever and a gyroscope. In addition, we focus on the development of the jerk sensor for seismic vibrations. The adopted methodology optimized the dimensions of an austenitic stainless steel cantilever and enhanced the performance in terms of sensitivity and the jerk measurable range. We found, after several analytical and FE analyses, that an L-35 cantilever model with dimensions 35 × 20 × 0.5 (mm3) and a natural frequency of 139 (Hz) has a remarkable performance for seismic measurements. Our theoretical and experimental results show that the L-35 jerk sensor has a constant sensitivity value of 0.05 ((deg/s)/(G/s)) with ±2% error in the seismic frequency bandwidth of 0.1~40 (Hz) and for amplitudes in between 0.1 and 2 (G). Furthermore, the theoretical and experimental calibration curves show linear trends with a high correlation factor of 0.99 and 0.98, respectively. These findings demonstrate the enhanced sensitivity of the jerk sensor, which surpasses previously reported sensitivities in the literature.

Optical monitoring of detergent pollutants in greywater.

Large amount of wastewater is produced by washing machines and dishwashers, which are used in a daily basis. This domestic wastewater generated in households or office buildings (also called greywater) is drained directly to the drainpipes without differentiation from that with fecal contamination from toilets. Detergents are arguably the pollutants most frequently found in greywater from home appliances. Their concentrations vary in the successive stages in a wash cycle, which could be taken into account in a rational design of home appliances wastewater management. Analytical chemistry procedures are commonly used to determine the pollutant content in wastewater. They require collecting samples and their transport to properly equipped laboratories, which hampers real time wastewater management. In this paper, optofluidic devices based on planar Fabry-Perot microresonators operating in transmission mode in the visible and near infrared spectral ranges have been studied to determine the concentration of five brands of soap dissolved in water. It is found that the spectral positions of the optical resonances redshift when the soap concentration increases in the corresponding solutions. Experimental calibration curves of the optofluidic device were used to determine the soap concentration of wastewater from the successive stages of a washing machine wash cycle either loaded with garments or unloaded. Interestingly, the analysis of the optical sensor indicated that the greywater from the last water discharge of the wash cycle could be reused for gardening or agriculture. The integration of this kind of microfluidic devices into the home appliances design could lead to reduce our hydric environmental impact.

A new measurement method for mixing and segregation of binary mixture by combining gas cutting-off method and ECVT

Binary mixture fluidization is an important operation type of fluidized bed. The regularity of mixing and segregation of binary mixture is the key to process research and development. In this paper, a new experimental method for the measurement of mixing and segregation of binary mixture is proposed by combining gas cutting-off method and Electrical Capacitance Volume Tomography (ECVT). While inheriting the advantage of high applicability of gas cutting-off method, this method can be of high spatial resolution with the application of ECVT. This combination can not only obtain detailed particle component distribution information, but also reduce the experimental labor intensity. Based on the measuring principle of ECVT and Maxwell Garnett mixing model, the calibration relationship between voxel value and particle mixing ratio was deduced. An experimental binary mixture system was employed to validate the calibration relationship. The result showed that the calculated calibration curve was in good agreement with the experimental calibration curve, which indicates the rationality and feasibility of the method. On this basis, mixing process of the same particle system was quantitatively characterized and analyzed at a given air flow rate. • A new measurement method for mixing and segregation of binary mixtures was developed. • High resolution of ECVT and high application of gas-cutting method were combined. • Measuring principle was mathematically derived for isotropic binary mixture. • Mixing process of experimental binary particle system was visually studied.

Exploiting a strong coupling regime of organic pentamer surface plasmon resonance based on the Otto configuration for creatinine detection.

The sandwiched material-analyte layer in the surface plasmon resonance (SPR)-Otto configuration emulates an optical cavity and, coupled with large optical nonlinearity material, the rate of light escaping from the system is reduced, allowing the formation of a strong coupling regime. Here, we report an organic pentamer SPR sensor using the Otto configuration to induce a strong coupling regime for creatinine detection. Prior to that, the SPR sensor chip was modified with an organic pentamer, 1,4-bis[2-(5-thiophene-2-yl)-1-benzothiopene]-2,5-dioctyloxybenzene (BOBzBT2). To improve the experimental calibration curve, a normalisation approach based on the strong coupling-induced second dip was also developed. By using this procedure, the performance of the sensor improved to 0.11 mg/dL and 0.36 mg/dL for the detection and quantification limits, respectively.

Heat Transfer Coefficient of Zinc Phosphate Coating with Metal Soap during Cold Backward Extrusion

In cold forging, the temperature difference between the workpiece and the tool is small and thus deformation analysis is rarely coupled with thermal analysis. However, the friction coefficient of zinc phosphate coating with metal soap has a large temperature dependence. Consideration of the effects of workpiece and tool temperature change on the friction coefficient is thus expected to improve the analytical accuracy of cold forging. Thermally coupled cold forging analysis requires thermal conductivity, specific heat, heat transfer coefficient between the workpiece and the tool, in addition to the temperature dependence of flow stress and friction coefficient. The heat transfer coefficient between workpieces coated with zinc phosphate with metal soap and tools is investigated in this paper. Cold backward extrusion was performed with a 50% reduction of area, and the temperature history in the punch was measured with a thermocouple. The forging speed was 1, 3, and 10 spm. FEM analysis was performed to simulate the experiment by considering the temperature dependence of flow stress and friction coefficient. The heat transfer coefficient was estimated at 20 kW/(m2•°C) by comparing the experimental result and calibration curves.

Y2O3:Eu and the Mössbauer isomer shift coefficient of Eu compounds from ab-initio simulations

We report on a full potential density functional theory characterization of Y2O3 upon Eu doping on the two inequivalent crystallographic sites 24d and 8b. We analyze local structural relaxation, electronic properties and the relative stability of the two sites. The simulations are used to extract the contact charge density at the Eu nucleus. Then we construct the experimental isomer shift (IS) versus contact charge density calibration curve, by considering an ample set of Eu compounds: EuF3, EuO, EuF2, EuS, EuSe, EuTe, EuPd3 and the Eu metal. The, expected, linear dependence has a slope of α = 0.054 mm s−1 Å− 3, which corresponds to nuclear expansion parameter ΔR/R = 6.0 × 10−5. α allows to obtain an unbiased and accurate estimation of the IS for any Eu compound. We test this approach on two mixed-valence compounds Eu3S4 and Eu2SiN3, and use it to predict the Y2O3:Eu IS with the result +1.04 mm s−1 at the 24d site and +1.00 mm s−1 at the 8b site.

Thickness mapping of organic layers applied on sterling silver by means of X-ray fluorescence scanning

The application of organic protective coatings is the most commonly used strategy to avoid tarnishing of silver artefacts. Non-invasive mapping of coating thickness is a key point to evaluate the consistency and completeness of coating application and to monitor protective effectiveness over time. This work is aimed at evaluating the feasibility of acquiring thickness maps of coated silver surfaces by means of X-ray fluorescence scanning. Experimental calibration curves were built by using organic films of known thickness made of several commonly used protective coatings, (Zapon nitrocellulose lacquer, and acrylic resins Paraloid B72 and Paraloid B44). X-ray fluorescence scans were performed on coated silver sterling coupons treated by brushing or spraying polymer solutions prepared with different solvents and concentrations. Thickness maps pointed out different average thickness values among selected polymers and highlighted that almost all the coatings were unevenly spread on the surface. Application by brush provided particularly uneven coatings and brush strokes were clearly distinguishable. The accuracy of thickness measurements was calculated by means of a semi-empirical approach, and results suggest that thickness can be predicted with an uncertainty of about ±5%.

Singular Value Decomposition for Removal of Cardiac Interference from Trunk Electromyogram.

A new algorithm based on singular value decomposition (SVD) to remove cardiac contamination from trunk electromyography (EMG) is proposed. Its performance is compared to currently available algorithms at different signal-to-noise ratios (SNRs). The algorithm is applied on individual channels. An experimental calibration curve to adjust the number of SVD components to the SNR (0–20 dB) is proposed. A synthetic dataset is generated by the combination of electrocardiography (ECG) and EMG to establish a ground truth reference for validation. The performance is compared with state-of-the-art algorithms: gating, high-pass filtering, template subtraction (TS), and independent component analysis (ICA). Its applicability on real data is investigated in an illustrative diaphragm EMG of a patient with sleep apnea. The SVD-based algorithm outperforms existing methods in reconstructing trunk EMG. It is superior to the others in the time (relative mean squared error < 15%) and frequency (shift in mean frequency < 1 Hz) domains. Its feasibility is proven on diaphragm EMG, which shows a better agreement with the respiratory cycle (correlation coefficient = 0.81, p-value < 0.01) compared with TS and ICA. Its application on real data is promising to non-obtrusively estimate respiratory effort for sleep-related breathing disorders. The algorithm is not limited to the need for additional reference ECG, increasing its applicability in clinical practice.

Intensity Fluctuations in Single-Molecule Surface-Enhanced Raman Scattering.

Around 20 years ago, the first reports of single-molecule surface-enhanced Raman scattering (SM-SERS) caused a revolution in nanotechnology. Several researchers were quick to recognize the importance of a technique that can provide molecular vibrational fingerprinting at the SM level. Since then, a large amount of work has been devoted to the development of nanostructures capable of SM-SERS detection. A great effort has also been geared toward elucidating the different mechanisms that contribute to the effect. The understanding of the concept of plasmonic SERS hotspots, the role of chemical effects, and the dynamics of atomic and cluster rearrangements in nanometric domains has significantly advanced, driven by new computational and experimental methods used to study SM-SERS. In particular, SERS intensity fluctuations (SIFs) are now recognized as a hallmark of SM-SERS. Interpretation of SM-SERS data must take into consideration temporal and spatial variations as a natural consequence of the extreme localization inherent to surface plasmon resonances. Further analysis of variations in spectral signature, due to either molecular reorientation or photo (or thermal) processes, pointed to a new area that combines the power of SERS fingerprinting at the SM level to modern concepts of catalysis, such as hot-electrons-driven chemistry. This large body of work on the fundamental characteristics of the SM-SERS effect paved the way to the interpretation of other related phenomena, such as tip-enhanced Raman scattering (TERS). Despite all the fundamental progress, there are still very few examples of real applications of SM-SERS. In recent years, our research group has been studying SIFs, focused on different ways to use SM-SERS. The obvious application of SM-SERS is in analytical chemistry, particularly for quantification at ultralow concentrations (below 1 nM). However, quantification using SM-SERS faces a fundamental sampling problem: the analytes (adsorbed in very small amounts, i.e., low surface coverage) must find rare SERS hotspots (areas with intense electric field localization that yields SERS). This limitation leads to strong temporal and spatial variations in SERS intensities, which translates into very large error bars in an experimental calibration curve. We tackled this problem by introducing the concept of "digital SERS". This approach provided a roadmap for SERS quantification at ultralow concentrations and a potential pathway for a better understanding of the "reproducibility problem" associated with SERS. In this Account, we discuss not only the analytical applications but also other implementations of SM-SERS demonstrated by our group. These include the use of SM-SERS as a tool to probe colloidal aggregation, to evaluate the efficiency of SERS substrates, and to characterize the energy of localized resonances. SERS involves a series of random processes: hotspots are rare; surfaces/clusters constantly reconstruct; and molecules diffuse, adsorb, and desorb. All these pathways contribute to strong fluctuations in SERS intensities. Our work indicates that a statistical view of the effect can lead to interesting insights and the potential to fulfill the promise of this SM technique for real-world applications.

Nanoscale Lithium Quantification in LiXNiyCowMnZO2 as Cathode for Rechargeable Batteries

Time-of-flight secondary ion mass spectrometry (TOF-SIMS) using a focused ion-beam scanning electron microscope (FIB-SEM) is a promising and economical technique for lithium detection and quantification in battery materials because it overcomes the limitations with detecting low Li content by energy dispersive spectroscopy (EDS). In this work, an experimental calibration curve was produced, which to our best knowledge allowed for the first time, the quantification of lithium in standard nickel manganese cobalt oxide (NMC-532) cathodes using 20 nm resolution. The technique overcomes matrix effects and edges effects that makes quantification complex. This work shows the high potential of TOF-SIMS tool for analytical characterization of battery materials, and demonstrates its tremendous capabilities towards identification of various chemical or electrochemical phenomena in the cathodes via high-resolution ion distributions. Various phenomena in the ion distributions are also assessed, such as edge effects or measurement artifacts from real signal variations.

Simultaneous Determination of Zidovudine and Lamivudine in Plasma Samples Using Miniaturized Homogenous Liquid–Liquid Extraction and High-Performance Liquid Chromatography

A simple and effective miniaturized homogenous liquid–liquid extraction technique coupled with a gradient HPLC method for the determination of lamivudine and zidovudine in the human plasma samples was developed. Separation of these drugs was performed on a C18 stationary phase by using a mixture of acetonitrile and water as mobile phase. Extraction of drugs was achieved based on salting-out phenomenon. Variables affecting the extraction efficiency, such as solvent type and its volume, type of salt and its concentration and sample pH have been fully evaluated and optimized. These drugs were successfully extracted by acetonitrile as extracting solvent with sodium sulfate as salting-out agent. Under the optimized experimental conditions calibration curves showed good linearity (r 2 > 0.9938) and precision (RSD < 6.7%) in the working concentration ranges. The limits of detection for lamivudine and zidovudine were 0.006 and 0.003 µg/mL, respectively. The limits of quantification for lamivudine and zidovudine were 0.02 and 0.01 µg/mL, respectively. The recoveries were in the range of 88.0–100.0% with RSD values less than 7.5%. The method was successfully applied to extract and determine lamivudine and zidovudine in human plasma.

Tunable direct beta-radiation harvester at the nanowatt scale

The feasibility of high efficiency direct energy-harvesting from a beta-current source is experimentally investigated by application of a dedicated apparatus. The idea is to collect the beta particles emitting from the source and convert these into usable electrical energy without intermediate steps. To this end an adjustable apparatus was designed and constructed, enabling to demonstrate, characterize and tune the rate of energy conversion under the constraints of small current (in the range of pA) and electric potential of up to 3800 V. At the center of the apparatus is a pair of moveable electrodes in which the beta-source is adhered to the positive one, and the emitted particles are collected on the opposite negative electrode. The energy conversion rate is controlled by adjusting the gap between the electrodes and the ultra-high resistance load circuit. Experimental setup, calibration curves and results are presented for a 15 mCi 63Ni radioisotope. Energy harvesting rates of 22.5 nW achieved in the experiments correspond to a 6.6% efficiency of the process.

Percent residual accuracy for quantifying goodness-of-fit of linear calibration curves

Linear models for calibration curves are overwhelmingly created based on minimization of least squares error, with their goodness-of-fit (GOF) quantified using the square of the correlation coefficient (R2). Yet, R2 has well-known disadvantages when used to quantify GOF of calibration curves stemming from its calculation based on the absolute error of the signal (i.e., calculated vs. experimental). These disadvantages are exacerbated when using a geometric series of concentrations for calibration standards (e.g., 1, 2, 5, 10, etc.) and when calibration curves span 2–3 orders of magnitude, which is typical for modern analytical techniques. While there are multiple alternative GOF measures, R2 overwhelmingly persists in the field of Analytical Chemistry as the most reported measure of GOF. We evaluated R2, alternative GOF measures, and multiple quantitative bias parameters, along with residual analysis, for over 60 experimental calibration curves. R2 did a poor job of consistently and accurately quantifying the GOF over the entire calibration curve. This was especially true for situations where the low concentration calibrators were not accurately described by the calibration equation. While other GOF parameters, including the sum of the absolute percent error, mean absolute percent error, and quality coefficient, did a better job of describing GOF of calibration curves, each had significant theoretical and/or practical disadvantages. Therefore, we introduce a descriptive GOF parameter called Percent Residual Accuracy (%RA or PRA) which equally weights the accuracy of all calibrators into a single value, generally falling between 0% and 100%, with 100% representing a perfect fit and a “good” fit for calibration data producing a %RA of 90–100%. The %RA much more effectively described the GOF for the entire calibration range than R2, and it similarly quantified GOF as compared to the other GOF parameters tested. With the performance and practical advantages of %RA, we conclude that it is the most advantageous GOF parameter and that it should be reported as a standard GOF measure for calibration curves.

Ordinary Least Squares with Laboratory Calibrations: A Practical Way to Show Students that This Fitting Model may Easily Yield Biased Results When Used Indiscriminately

Analytical calibration using ordinary least squares (OLS) is the most widely applied response function for calibration in all type of laboratories. However, this calibration function is not always the most adequate and its indiscriminant use can lead to obtain biased estimates of unknowns. Students need to be taught about the practical requirements needed to obtain good results with OLS and when this fitting method is not accurate. Different experimental calibration curves were obtained in laboratory sessions using two common instrumental techniques: chromatography and atomic absorption spectrometry. After discussion seminars evaluating the data obtained by students, they were able to understand that linear fitting was not the most accurate model using atomic absorption spectrometry and a quadratic fitting provided most accurate estimates. Linearity was confirmed in chromatographic calibrations, but data presented heteroscedasticity, which is very common in calibrations done in chemical and biological analyses. A simple experiment was applied to show students how the use of the regression coefficients obtained by OLS with heteroscedastic data lead to highly biased estimates near the quantification limits of the calibration curve. The results obtained allowed to show students that, despite being widely used, OLS is not the most adequate fitting model to obtain accurate and precise results with many calibration methods routinely used in chemical and biological laboratories.

Regional long-period magnitude scales and their capabilities for tsunami warning

The tsunami warning system in the Russian Far East employs the medium-period magnitude MS (BB) by Vaniek–Soloviev. However, its use may lead to inadequacies and underestimates for the tsunamigenic potential of an earthquake. Specifically, this can happen in the case of a so-called tsunami–earthquake. This kind of earthquakes with a nonstandard spectrum was revealed by H. Kanamori in 1972. This problem can be overcome by using a magnitude scale that deals with longer period seismic waves. This study develops a technique for determining the magnitudes at regional distances (from 70 to 4500 km) using the amplitudes of surface seismic waves of periods of 40 and 80 s. At distances of 70–250 km, the amplitude of the joint group of shear and surface waves is used. For the new magnitudes designated M S(40) and M S(80), experimental calibration curves are constructed using more than 1250 three-component records at 12 stations of the region. The magnitudes are calibrated so as to produce an unbiased estimate of the moment magnitude M w in the critical range 7.5–8.8. The rms error of the single-station estimate M w is around 0.27. At distances below 250 km and M w ≥ 8.3, the estimate of M w obtained by the proposed technique becomes saturated at the level of M w ~ 8.3, which is acceptable for operative analysis because no missed alarms arise. The technique can be used in operational tsunami warning based on seismological data. This can markedly decrease the number of false alarms.

Magnetically tuned mass dampers for optimal vibration damping of large structures

This paper deals with the theoretical and experimental analysis of magnetically tuned mass dampers, applied to the vibration damping of large structures of civil engineering interest. Two devices are analysed, for which both the frequency tuning ratio and the damping coefficient can be easily and finely calibrated. They are applied for the damping of the vibrations along two natural modes of a mock-up of a bridge under construction. An original analysis, based on the Maxwell receding image method, is developed for estimating the drag force arising inside the damping devices. It also takes into account self-inductance effects, yielding a complex nonlinear dependence of the drag force on the velocity. The analysis highlights the range of velocities for which the drag force can be assumed of viscous type, and shows its dependence on the involved geometrical parameters of the dampers. The model outcomes are then compared to the corresponding experimental calibration curves. A dynamic model of the controlled structure equipped with the two damping devices is presented, and used for the development of original optimization expressions and for determining the corresponding maximum achievable damping. Finally, several experimental results are presented, concerning both the free and harmonically forced vibration damping of the bridge mock-up, and compared to the corresponding theoretical predictions. The experimental results reveal that the maximum theoretical damping performance can be achieved, when both the tuning frequencies and damping coefficients of each device are finely calibrated according to the optimization expressions.