dataesr/scientific-paragraphs-categorization huggingface dataset:
Design Methodology In an initial effort to minimize the resolvable pressure of the PGM system, a balance must be found between in , Q, and AR; furthermore, both the readout coil and the LC sensor must be designed in tandem in order to ensure M is large enough such that in is above the minimum detectable signal for the utilized readout electronics. In designing the LC sensor, there are two main factors that must be considered: (1) f 0 is extracted through curve fitting of discrete data points; and (2) in , Q and AR are often in direct conflict-as one is increased, the other is reduced. (For example, Q can be increased by adding parallel capacitance, additional C Par , to reduce R S contributed by transducer ESR, but this addition will result in a reduction in AR). This inverse relationship between AR and Q balances RR which depends on the product of these terms.
The final design parameters must remain within the physical boundary conditions of the specific application; the boundary conditions applicable to the core-flood experiments targeted in this work are shown in Table  2. The frequency range over which Re{Z in } is measured from an LC sensor (i. e. , the interrogation frequency range, IFR) must be wide enough so that curve fitting to extract f 0 can be completed. To capture >99% of the sensor readout across the full-scale pressure, a frequency range of approximately ±3σ plus the span of ∆f 0 is required. In addition to IFR, the step size between data points (frequency step size, f ss ) must be chosen. With fewer data points, data collection bandwidth can be improved but curve fitting performance (and resolution) may decrease. Empirical testing with the implemented curve fitting algorithm within the R-Pi microcomputer revealed that performance saturated beyond a data density of approximately 10 points within the LC sensor frequency response bandwidth, σ (f 0 /Q).
Therefore, a f ss of σ/10 (f 0 /(10 Q)) is used. The bandwidth normalization in RR facilitates the comparison of different LC sensor AR, to select the most suitable design. With the discrete nature of the captured dataset of Re{Z in }, it is important to note that, regardless of absolute values of Q and σ, the Re{Z in } datasets for all LC sensors look identical over their IFR, when bandwidth normalization is utilized. Therefore, the frequency shift due to applied pressure for a given LC sensor design may be then easily expressed in terms of their respective signal bandwidth, as shown in Figure  3. As previously discussed, in addition to AR and Q (now captured in RR), in also impacts resolution. Assuming a constant white noise across the frequency spectrum, in is directly proportional to SNR.
e. , a 2× increase in in will improve resolution by ≈2×). in Table  3. Re{Zin} is plotted over each designs normalized bandwidth, σ. 3 ). All datasets plotted over their bandwidth (i. e. , bandwidth normalized). The design maximizing AR has the smallest RR (as its σ is >14x larger than the other designs). The design maximizing RR results in the smallest Rin (due to high RS of its LS coil). However, the design maximizing FOM, which is the product of RR and Rin, Equation  (13) , results in middling RR and Rin values, shown in green. This parameter is suitable as a figure of merit because it accounts for the combined impact of Q and AR through RR, Equation (  12 ), as well as the impact of Rin on the SNR. The design with the maximum value returned by the FOM is the LC sensor design which will theoretically permit the minimum resolvable pressure.
The inverse of FOM is proportional to pressure resolution; when a fitted proportionality factor, C, is included, absolute values for predicted pressure resolution, ResolSim, can be found, Equation  (14). An LC sensor design optimization program was implemented in MATLAB to sweep physical LC sensor and readout coil design parameters within their boundary conditions (Table  2 ) and automatically calculate the FOM for each design. Approximations for inductance  [36]  and coil resistance  [37]  were utilized while k and M were determined through finite element analysis (FEA) modeling in the absence of accurate closed form equations. 3 ). All datasets plotted over their bandwidth (i. e. , bandwidth normalized). To further illustrate how Q, RR, AR, and in are interconnected, Figure  3  shows a SPICE simulation of three Re{Z in } datasets for different designs using parameters as listed in Table  3.
The inverse of FOM is proportional to pressure resolution; when a fitted proportionality factor, C, is included, absolute values for predicted pressure resolution, Resol Sim , can be found, Equation (  14 ). An LC sensor design optimization program was implemented in MATLAB to sweep physical LC sensor and readout coil design parameters within their boundary conditions (Table  2 ) and automatically calculate the FOM for each design. Approximations for induc-tance  [36]  and coil resistance  [37]  were utilized while k and M were determined through finite element analysis (FEA) modeling in the absence of accurate closed form equations. The simplified closed form equations that may be used to estimate M do not account for the magnetic coupling and leakage flux  [38] ; additionally, the magnetic behavior of the packaging materials and other nonidealities have an impact on the value.
Consequently, FEA was utilized in COMSOL Multiphysics ® to extract M between L S and L RO. Figure  4  shows the 2D axisymmetric model geometry, including the package structure; materials of the LC sensor are noted detailed in Section 3 and listed in Table  4. The magnetic field lines between readout coil and LC sensor at an excitation frequency of 13 MHz are also shown in Figure  4. The field lines show the impact of the top metal cover and bottom metal stiffener: the bending of field lines in the presence of these elements reduces coupling between the LC sensor and center of the readout coil, moving the maximum field line concentration to the outer edge of the LC sensor coil. The simplified closed form equations that may be used to estimate M do not account for the magnetic coupling and leakage flux  [38] ; additionally, the magnetic behavior of the packaging materials and other nonidealities have an impact on the value.
Consequently, FEA was utilized in COMSOL Multiphysics ® to extract M between LS and LRO. Figure  4  shows the 2D axisymmetric model geometry, including the package structure; materials of the LC sensor are noted detailed in Section 3 and listed in Table  4. The magnetic field lines between readout coil and LC sensor at an excitation frequency of 13 MHz are also shown in Figure  4. The field lines show the impact of the top metal cover and bottom metal stiffener: the bending of field lines in the presence of these elements reduces coupling between the LC sensor and center of the readout coil, moving the maximum field line concentration to the outer edge of the LC sensor coil. 4. In the context of this work, measured pressure resolution, ResolMeas, is defined as the RMS error (e. g. , ±standard deviation) of at least 50 readings (n ≥ 50) of the LC sensor taken by the PGM system.
The measured pressure resolution, ResolMeas was calculated by the RMS error of f0 by AR converting to units of pressure,  (15) : 𝑅𝑒𝑠𝑜𝑙 = 𝑅𝑀𝑆 𝐸𝑟𝑟𝑜𝑟 𝑜𝑓 𝑓 𝐴𝑅 (15) In order to verify FOM, four types of LC sensors were fabricated on planar inductor coils using varying PXDCR protection materials, and ceramic capacitors were electrically connected in parallel with CXDCR to emulate varying CPar values, resulting in ten distinct designs. A total of 50 discrete datasets for all ten designs were taken and used to both calculate ResolMeas and fit CPar and RS,Coil values to the LC sensor circuit in Figure  2a  (using previously measured values for CXDCR, ESR, LS, and LRO) via SPICE modeling. These component values were coded into the MATLAB model and the additional CPar value was swept from 0 to 50 pF while returning ResolSim (C = 1).
The ResolSim and ResolMeas for these designs are plotted in Figure  5 , showing an agreement within 11%, confirming the ability   4. In the context of this work, measured pressure resolution, Resol Meas , is defined as the RMS error (e. g. , ±standard deviation) of at least 50 readings (n ≥ 50) of the LC sensor taken by the PGM system. The measured pressure resolution, Resol Meas was calculated by the RMS error of f 0 by AR converting to units of pressure, (15): Resol Meas = RMS Error o f f 0 AR (15) In order to verify FOM, four types of LC sensors were fabricated on planar inductor coils using varying P XDCR protection materials, and ceramic capacitors were electrically connected in parallel with C XDCR to emulate varying C Par values, resulting in ten distinct designs. A total of 50 discrete datasets for all ten designs were taken and used to both calculate Resol Meas and fit C Par and R S,Coil values to the LC sensor circuit in Figure  2a    The study yielded the preferred LC sensor design-corresponding to the highest value of FOM, which was 0.
These four voltages are then used to determine Re{Z in } at each discrete interrogation frequency. After completion of each measurement cycle, Re{Z in } values stored in the MCU Flash memory are transferred to the R-Pi, where a Python-based program extracts f 0 by applying a Gaussian fit, F Zin , to the Re{Z in } dataset. The total time required by the PGM system to trigger four readout nodes into a measurement cycle, interrogate each LC sensor over the IFR, transfer the Re{Z in } dataset from the MCU at each node to the R-Pi, and extract f 0 by fitting F Zin , was 25 s. The ability of the PGM readout system to accurately extract LC sensor f 0 was characterized using twelve LC sensor loads with f 0 values between 3 and 12 MHz as measured using a benchtop network analyzer (Keysight E5061B).
Use of a single, more computationally capable MCU would enable fitting algorithms to run real-time on the readout PCB itself, significantly reducing the requirement on external processing and data transfer. In terms of the present circuit, an external Flash memory at each readout node could also eliminate the need for repeated communication with the external unit at each frequency step through local data storage, thus reducing overall interrogation time. The approach used to design the PGM system has notable features that can extend its use to other applications. This work is scalable to a large number of sensors because of the use of a modular readout PCB design. The system hardware and software were codesigned to maximize pressure resolution. For instance, the LC sensor design which maximizes pressure resolution was selected while operating within the capabilities of the deployed curve fitting algorithm.
Use of a single, more computationally capable MCU would enable fitting algorithms to run real-time on the readout PCB itself, significantly reducing the requirement on external processing and data transfer. In terms of the present circuit, an external Flash memory at each readout node could also eliminate the need for repeated communication with the external unit at each frequency step through local data storage, thus reducing overall interrogation time. The approach used to design the PGM system has notable features that can extend its use to other applications. This work is scalable to a large number of sensors because of the use of a modular readout PCB design. The system hardware and software were co-designed to maximize pressure resolution. For instance, the LC sensor design which maximizes pressure resolution was selected while operating within the capabilities of the deployed curve fitting algorithm.
The LC sensor was comprised of a package using 3D printed aluminum caps to protect the capacitive transducer, encased in Viton TM and thin film polyimide to provide chemical resistance to the environment and still permit pressure transduction to the transducer, and measured <ø15 × 3 mm 3. The LC sensor interrogation system was comprised of a readout PCB and external unit containing a microcomputer and remote laptop with custom GUI for user control. Interrogation electronics extracted and digitized the real part of the input impedance, Re{Z in }, of an inductor coil inductively coupled to the LC sensor over a set of discrete frequencies. This discrete dataset was then transferred to the external microcomputer which extracted the pressure-dependent resonant frequency, f 0 , via interpolation by fitting a continuous Gaussian curve.
(using previously measured values for C XDCR , ESR, L S , and L RO ) via SPICE modeling. These component values were coded into the MATLAB model and the additional C Par value was swept from 0 to 50 pF while returning Resol Sim (C = 1). The Resol Sim and Resol Meas for these designs are plotted in Figure 5, showing an agreement within 11%, confirming the ability of the proposed FOM to reliably predict pressure resolution. Whereas the resolution of the unprotected LC sensor varied from ≈1 mbar (with 0 pF additional C Par ) to ≈1. 5 mbar (with 50 pF additional C Par ), the resolution of an LC sensor protected with 15-5 stainless steel had a larger variation from ≈2 mbar (with 0 pF additional C Par ) to ≈12 mbar (with 50 pF additional C Par ). Sensors 2023, 23, x FOR PEER REVIEW 9 of 21 of the proposed FOM to reliably predict pressure resolution.
Acknowledgments: The authors thank  T. Ben-Ghzaiel  and  A. Chenieux , at  Total Energies , for aid in facilitating project development. The authors thank  V. K. Gondrala  and the research team at  University of Cincinnati  for assistance in development of the PGM system readout nodes. Facilities used for this work include the  Lurie Nanofabrication Facility (LNF)  at the  University of Michigan ,  Ann Arbor.
) position-sensitive detector. The electron detector provided triggers for the pulsed ion extraction voltage U S across the source region of a modified Wiley-McLaren type ion time-of-flight (TOF) spectrometer. 43 he ions were then accelerated to the final energy by the drift tube voltage U A , first passing a lens element with U L , modifying the radial distribution and focusing. Ions were detected by a Roentdek 80 mm MCP and HEX-anode detector, recording ion flight times (TOFs) and radial hit positions. The electron energy and ion TOFs and ion positions of impact data were combined into a coincidence dataset. In addition, non-coincident ''random'' triggers for ion extraction were generated at a constant rate, interleaved with the ''true coincidence'' electron triggers. The ions collected using the random triggers were used for statistical subtraction of the false coincidence background from the ion TOF spectra, electron-ion-ion coincidence maps, coincident ion pair yields and ion momentum distributions.
The electron energy window for a coincidence dataset is determined by the electron acceleration/retardation and the pass energy E p of the analyzer, its width being about 8% of E p. Two electronmulti-ion coincidence datasets were collected, one with the Auger electron from the decay of the iodine 4d À1 vacancy created by 110 eV photons (we'll refer to it as AEPIPICO) and another with the iodine 4d photoelectron in coincidence with ions, using the 85 eV photon energy (the PEPIPICO dataset). In addition, a noncoincident ion TOF spectrum recorded at hn = 21 eV is reported. Pass energy of 200 eV and entrance slit of 0. 5 mm was used in the AEPIPICO measurement, giving the estimated electron energy resolution of about 250 meV full-width at half-maximum (FWHM). The photon flux was adjusted to give a low average electron count rate of 24 el s À1.
Ion-ion coincidences, dissociation pathways and ion pair yields A more detailed picture of the dissociation pattern of multiply charged molecules is obtained by coincident analysis of the emitted ions. Fig. 4  shows the ion pairs in the PEPIPICO dataset, detected in coincidence with the iodine 4d photoelectrons. They are presented in the form of an ion-ion coincidence (PIPICO) map, where the flight times of the faster and slower ionic fragments in the ToF spectrometer are given by the x and y-axis, respectively. The number of ion pairs is color-coded in the 2D histogram. The third particle -the photoelectron detected in coincidence -acts as a filter in this presentation, allowing only the dissociation events arising from the 4d ionization. There is, however, a chance of combining electrons and ions from different ionization events, if those occur sufficiently close in time.
Such ''false'' coincidences were subtracted from the PIPICO map  using a random-triggered dataset measured simultaneously as interleaved with the electron triggers. A precise false background subtraction from a histogram representing a multi-particle coincidence requires a number of parameters to be known about the detection efficiencies etc. 44  Here, we applied a more empirical technique where background PIPICO maps were constructed from (a) the random-triggered data and (b) combining ions from electron-and random-triggered data. These maps were then subtracted from the electron-triggered map, fine-tuning the multiplication coefficients by checking the intensity of the features that can only be false coincidences. For what will be our main region of interest, the (R + ,I + ) ion pairs, the coincidence purity was the following: 26 times more (R + ,I + ) ion pairs were produced in coincidence with the I 4d photoelectrons than by random triggers; 6.
The lower panel of Fig. 5  shows the CIPY curves of some of the most prominent ion pairs seen in the PIPICO map of Fig. 4. Since in the AEPIPICO measurement, the electron energy window was limited to about 16 eV, two separate datasets were recorded in order to cover the entire Auger spectrum. The datasets were joined together for the CIPY curves in Fig. 5 , with the dataset separation energy shown as the dashed vertical line. One can see that, moving in the direction of increasing internal energy, after the M ++ signal disappears, the first dissociation channel to open is (R + ,I + ). At higher internal energies, the ring starts to fragment, thus suppressing the (R + ,I + ) CIPY. The (I + ,I + ) CIPY is quite weak in the covered energy region, but it was a strong pattern in the overview PIPICO map.
dT I dT R ¼ À MðRI þ Þ MðR þ Þ ¼ À2 The GSD model predicts also the magnitudes of the momenta of the dissociation products, and as a consistency check, they are compared with the experimental momentum distributions from the PEPIPICO dataset in Fig. 8. The curves were extracted from (R + ,I + ) ion pairs and show quite broad momentum distribution for the R + fragment and a narrower one at higher values for I +. Also marked at the figure are the GSD model values at the concerted and ''pure'' secondary dissociation limits, calculated using the I + ,RI + initial separation of 4. 07 Å. The shaded ranges correspond to the transition region, where the R + momentum increases and I + momentum decreases. As can be seen, these ranges fit very well with the observed distributions. 3. 3. 3 Secondary dissociation timescales and dependency on the internal energy.
We can now return to the slope analysis results of the AEPIPICO data. They are shown as red circles in Fig. 9  as the tilt angle of the (R + ,I + ) pattern from subsets of the AEPIPICO data, for various ranges of the Auger electron kinetic energy. The corresponding internal energy range is given by the top axis. The datapoints are averages of the three values obtained from the AEPIPICO patterns constructed using the flight time and the x and y coordinates. The data error bars reflect both the statistical uncertainty and the variations when using different fitting models for determining the tilt angle that gives the narrowest projected PIPICO pattern. One can see that at the lowest internal energies, near the appearance threshold of the (R + ,I + ) ion pair (Fig. 5 ), the angle approaches that of ''pure'' secondary dissociation (dashed horizontal line), where T sec -N.
The model as presently applied is a stochastic charge hopping molecular mechanics model with simplified force field of molecular bonding. This light approach is focused on the asymptotic result and allows to generate large datasets directly comparable with the experiment. The detailed description and the used parameters are given in the ESI, † but the essential elements in modeling the fragmentation dynamics are: 1. Atoms are treated as point charges with integer values (0, +e, +2e). The total molecular charge was +2e and the initial allocation of the charges was randomized, giving a basic representation of the broad range of the final states of the Auger transitions. 2. The +e unit charges are allowed to hop across the bonds between atoms during the dynamics. The hops are cut off at a critical distance given by the classical over-the barrier model.
No torsional forces were included. As non-bonding interactions, a repulsive potential wall was created around atoms. At the start of the dynamics, the molecule was given internal energy, in addition to the potential energy of the coulombic repulsion of point charges. It is in the form of randomly distributed atomic velocities (internal temperature). Randomization for simulated datasets was obtained by (i) choice of the initial atomic velocities, (ii) initial charge localizations and (iii) redistributing charges across bonds during the hops. First, let us confirm whether the stochastic charge hopping molecular mechanics model gives a reasonable statistical description of the overall dissociation landscape. Fig. 10  shows a comparison of a simulated PIPICO map with the experimental one. The fragment mass and charge values together with the p z projections of their momenta were converted to the ion TOF values, using the conversion factors corresponding to our experimental conditions.
The ion momenta were obtained in the fixed frame-of-reference for the Monte Carlo dataset of trajectories, but random, isotropic frame rotation was then applied for each trajectory in order to simulate the isotropic orientation of the gas-phase molecules in the experiments. The simulation was run with E int = 7 eV initial energy. However, charge hopping in this point-charge model tends to create a drift towards higher internal energies, due to the molecule starting to accommodate its geometry to a particular charge configuration between the hops, which then tend to bring the molecule into an energetically less favourable geometry. This slight random drift causes the trajectories to be generated for a range of eventual internal energies. Although at first glance a completely unphysical feature, such energy drift can in fact be regarded as a basic representation of internal energy conversion, where the seemingly external energy source would be the relaxation of the electronic structure that is not a part of this model.
Let us now concentrate once again on the main pathway of interest, RI 2 ++ -R + + I + + I. The insets of Fig. 10  show the corresponding PIPICO pattern for both the simulated and experimental dataset. It appears to have a larger slope in the latter, suggesting that the simulation is not well describing the momentum sharing. However, this discrepancy is explained below after scrutinizing the time information from individual trajectories. While for the experiment, the secondary dissociation time T sec was deduced from the tilt angle of the (R + ,I + ) pattern using the GSD model, in the simulation the bond breakage times are directly available from each trajectory. Fig. 11  shows the histogram of the I-C bond breakage times, for yielding both the charged and neutral atomic iodine fragments. Same as for the PIPICO map, the analyzed trajectories were restricted to the E int o 9 eV range.