11institutetext: CERENA, Instituto Superior Técnico, Universidade de Lisboa
Av. Rovisco Pais, 1049-001 Lisboa, Portugal
11email<EMAIL_ADDRESS>
# Air pollution models in epidemiologic studies
with geostatistics and machine learning

Manuel Ribeiro 11 0000-0002-7890-7708

###### Abstract

Development of air pollution models for large regions is a priority for
population-based epidemiologic studies. The rapid development of big data
information systems and machine learning algorithms have opened new grounds
for refinements of current model frameworks. This commentary overviews recent
contributions and outlines extensions from geostatistics and machine learning
perspectives. For the coming years, expected advances will expand the use of
learning algorithms to model spatial trends and incorporate spatial covariance
models in the learning processes. These extensions will refine existing
modelling frameworks contributing to improve accuracy of air pollution models
for exposure assessment.

###### Keywords:

geostatistics machine learningair pollution exposure

##  1 Overview

Cancers, respiratory and cardiovascular diseases have been the most common
reasons for premature death attributable to air pollution around the globe.
Still, apart from few exceptions, there is not a definite knowledge about the
impacts of air pollution on populations. Therefore the development of accurate
models predicting air pollution is critical to better understand the health
impacts of exposure to air pollution.

Air pollution models require data collection, which often comes from
monitoring by local or national institutions [6], using a set of ground-based
stations geographically dispersed over locations selected for regulatory
purposes. Although these provide air pollutants information with high accuracy
and high temporal resolution, they are sparse in space and have limited area
coverage. In such cases, monitoring data can be combined with statistical
models predicting pollution gradients between ground-based monitoring stations
and in areas where no stations exist at all [32]. To this end, remotely sensed
data [14], chemical transport models [15] or Copernicus Atmospheric Monitoring
Service (CAMS) products [26] have been used. Similarly, meteorological data
[35] and other auxiliary information such as land-use data or distance to
roads are useful for modelling, predict and describe smaller-scale variations
[5, 30] and are a solution to obtain high-resolution air pollution maps [29,
33].

More traditional modelling approaches are supported on linear combinations of
potential predictors while considering their geographic locations. More
specifically, land use regressions [17, 21], employ linear regression
combining geographic data collected on air pollutants and on potential
predictors, and are a relatively cheap and practical approach to predict air
pollution exposure in urban areas; spatial regressions [36, 38], allow linear
regression parameters to vary smoothly as a function of spatial neighborhoods
and increase the potential to capture non-stationary relations between
predictors and air pollutants; geostatistical models [24, 33], take into
account for spatial trends and spatial autocorrelation, capturing the
intensity and direction of the spatial processes underlying air pollution
concentrations, which is especially relevant in large regions, where
variations in their intensity and direction are likely to occur.

In the last decade, approaches integrating machine learning techniques have
been applied to handle non-linear interactions between predictors [2], and
hybrid models have been developed to account for spatial dependence of air
pollutants [28]. Mostly focused on supervised learning, some popular
algortihms used are decision trees [7], random forest [19, 37], artificial
neural networks [1]. Compared to the abovementioned linear models, the
superiority of these algorithms relies in their capability to deal better with
such complex non-linearities [18].

In addition to air pollution mapping, assessment of spatial uncertainty of
predictions (hereinafter referred to as "uncertainty") is also required, since
predictions have uncertainty that typically varies spatially and temporally.
In fact, quantifying uncertainty (e.g. prediction intervals) provides
important information about the prediction error in the air pollutant values
used in exposure assessment, and addresses spatial misalignment of pollutant
and health data [9]. Not taking uncertainty into account may produce
misleading conclusions about the potential impacts on population´s health and
weaken the scientific validity of its findings. In Bayesian framework,
uncertainty can be assessed using a hierarchical formulation [3], or with
geostatistical algorithms using stochastic modelling [23]. Yet, quantification
of spatial uncertainty is still in its infancy.

After this first section with an overview, the commentary is divided into 2
sections (second, third): section 2 presents some basics on geostatistical and
machine learning and section 3 present some possible ways ahead in the
perspective of combining geostatistical and machine learning methods.

##  2 Models

While geostatistics is widely used in environmental applications of spatial
data modelling, it shows at the same time, difficulties to model non-linear
and complex dependency structures. Similarly, machine learning algorithms are
powerful tools to solve complex real-world problems. Yet, they usually do not
consider sample locations and spatial autocorrelation in the learning process.
Combining the strengths of both approaches extends the existing methods to
model air pollution, which is challenging due to the complex non-linear
physical and chemical underlying processes and interactions affecting air
pollution concentrations at different spatiotemporal scales.

###  2.1 Geostatistical models

Geostatistics includes a set of statistical techniques suited to model
spatially correlated data, providing optimal unbiased predictions with minimum
mean squared prediction error. Geostatistics are based on the assumption that
sample data are a single realization of a spatial random process,
$Y(\mathbf{s})$. In (1), $S$ represents a finite spatial continuous domain and
$\mathbf{s}$ is a spatial index.

$\\{Y(\mathbf{s}):\mathbf{s}\in S\\}$ (1)

To model spatially correlated data variogram functions are used, and Kriging
(univariate case) or Cokriging (multivariate case) estimators are used for
optimal linear prediction. There are different types of Kriging techniques,
and the choice depends on data available and the aims of analysis. For a
comprehensive review about Kriging methods please refer to [8].

When the the objective of analysis is to assess spatial uncertainty, Kriging
techniques are not adequate. Specifically, Kriging techniques aim at
minimizing the prediction error, and this involves smoothing data variability.
To assess spatial uncertainty, geostatistical algorithms using stochastic
simulations should be preferred, as they reproduce the fluctuations observed
in the sample data, instead of producing optimal prediction. These algorithms
generate simulated maps with similar statistical properties to those of the
observed data (e.g., histogram, spatial covariance), and have been applied to
quantify air quality uncertainty to assess health impacts of exposure [23, 24,
34].

###  2.2 Machine learning models

In the past decade, many different learning algorithms have been applied to
model air pollution for epidemiology studies [2]. Currently, these models use
modelling frameworks with high dimensional input spaces including features
such as air pollution (e.g., remotely sensed data, CTM, ground-based
stations), meteorological-based models (e.g., temperature, wind, pressure,
humidity), other relevant biophysical and socio-economic information (e.g.,
land-use land cover, seasonality, elevation, distance to roads, population
density).

Mostly focused on supervised learning regression algorithms, the learning
processes use the input features, $\mathbf{x}$, for training and choose a
function $f(\mathbf{x})$ with parameters $\mathbf{w}$, that better fits the
output values $y$. To decide which function $f(\mathbf{x,w})$ provides the
best approximation to $y$, a measure of loss is computed. A popular choice for
regression problems is to tune $\mathbf{w}$ from data with the following loss
function, $L$:

$L[y,f(\mathbf{x,w})]=[y-f(\mathbf{x,w})]^{2}$ (2)

Random forest are one of the most used algorithms to predict air pollution
[25], as they provide an excellent trade-off between interpretability and
performance, when compared with other learning methods (e.g., neural networks,
support vector machine). Simpler learning methods such as linear regression
approaches have a long tradition in science and are also widely used [18].
Following the principle of Occam’s razor, when no major interactions and
approximately linear associations exist, these models perform well and should
be preferred instead of more complex algorithms. Neural networks are more
complex than the abovementioned algorithms and are more efficient to solve
non-linear problems. Several algorithmic variants are well established (e.g.,
artificial, convolutional neural networks) in air pollution modelling [27]. In
complex non-linear and high dimensional feature spaces, this algorithm can
achieve high model performances [4].

The trade-off between model performance and the ability to generalize is a
relevant issue to be taken into consideration, as the models should avoid
overfitting the data (leading to low performance on new data). Moreover, the
better performance of some complicated models may be hard to interpret. For
example, in neural networks or in support vector machine algorithms, transfer
functions and kernels used to fit the data, are rather artificial models and
hard to interpret. A clearer understanding of the internal mechanisms leading
to some output will contribute to improve their use in environmental
applications.

##  3 Geostatistical learning models

A modelling framework combining geostatistical and machine learning methods
and explored in recent years, relies on the grounds of supervised machine
learning with Regression Kriging [12], also known as Kriging with External
Drift (KED). Typically, KED decomposes a spatial random process
$Y(\mathbf{s})$ into a function representing the deterministic part of the
variation (e.g. linear model), $m(\mathbf{s})$, and a stochastic residual,
$r(\mathbf{s})$, describing the spatially dependent part of variability:

$Y(\mathbf{s})=m(\mathbf{s})+r(\mathbf{s})$ (3)

Real-world environments are highly non-linear and exhibit spatially dependent
underlying processes. Therefore, simplistic linear model assumptions like
linearity and independency are a limitation of traditional KED technique. The
performance of the deterministic part, $m(s)$, can be improved with machine
learning algorithms, as they do not require normally distributed data and are
able to handle interactions and non-linear relationships between input
features. In regression settings, random forests [19] or neural networks [27]
are some of the algorithmic models that could be applied to model the
deterministic part of RK.

####  3.0.1 Uncertainty

The spatial covariance model of the residual part, $r(s)$ can be inferred from
data and incorporated in a geostatistical simulation algorithm to predict
(map) spatially continuous surface of air pollution residuals. In fact, these
simulation algorithms do characterize the spatial parameters of interest
providing the means to generate realizations that reproduce spatial
anisotropic correlation structure, and the empirical histogram of the
stochastic residuals, $r(s)$. Then, simulated results are added to the
learned/tuned model outputs to create the final simulated air pollutant maps.
Two key maps may be drawn from the set of geostatistical simulations: a
pointwise median map of the variable of interest and the spatial uncertainty
attached, which can be quantified by the pointwise interquartile range (IQR).

Parallelization and multithreading processes can be used as optimization
techniques to run geostatistical simulation algorithms. In fact, these
algorithms provide a measure of spatial uncertainty but at a computational
high cost [10]. Several geostatistical simulation algorithms are available to
assess spatial uncertainty of predictions (e.g., Sequential Gaussian
Simulation, Direct Sequential Simulation, or Turning Bands). The performance
of different algorithms can be measured using some loss-function, to select
the best model. Readers may refer to Gómez-Hernandez and Srivastava [11] for a
comprehensive review of simulation algorithms.

####  3.0.2 Further extensions

The ability to model and predict accurately air pollution with spatial data
can be further refined by extending research in the perspective of machine
learning tunning parameters. It is well known that air pollution exhibits
spatial trends and spatial autocorrelation [22]. Relying on learning
approaches based on random samples of spatial data to tune model parameters,
fails to assess a model’s performance in terms of spatial mapping and only
validate its ability to reproduce sampling data [20]. Therefore, instead of
leaving spatial interactions to be learned from data, spatial parameters or
functions (e.g., geostatistical semi-variogram) could be used as tuning
parameter within the algorithmic model to control the optimal space and time
ranges to sample [13]. The idea can easily be extended to models aiming at
predicting in the multipollutant case [31], by extending the spatial tunning
parameters to cross-variograms.

The influential work of Kanevski [16] offers a solid perspective on the
grounds of machine learning developments for analysis and modelling of spatial
environmental data, that can be easily transferable to air pollution
modelling. In future work, researchers could extend the model framework,
considering refinements in the assessment of spatial uncertainty, and
optimization of parameters tunning by incorporating spatial properties of
data.

####  3.0.3 Aknowledgements

Manuel Ribeiro acknowledges Fundação para a Ciência e Tecnologia for the
research contract IF2018/CP1384/IST-ID/175/2018 and CERENA pluriannual funding
FCT-UIDB/04028/2020.

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# On the detection of alpha emission from a low-voltage DC deuterium discharge
with palladium electrodes

Erik P. Ziehm  Department of Nuclear, Plasma, and Radiological Engineering,
University of Illinois Urbana-Champaign, Urbana, 61801, USA
<EMAIL_ADDRESS>George H. Miley  Department of Nuclear, Plasma, and
Radiological Engineering, University of Illinois Urbana-Champaign, Urbana,
61801, USA

###### Abstract

The possibility of nuclear reactions within a low voltage DC deuterium
discharge with palladium electrodes is addressed. The solid-state nuclear
track detector known as CR-39 was chosen to investigate the emission of
energetic charged particles from the electrodes. A partially automated imaging
platform and feature classification process was developed to scan the CR-39
surface and detect tracks. Typical discharge parameters were 10 Torr
deuterium, 5-7 mm electrode gap distance, 20-40 mA/cm2 current density, and
-500 $\pm$ 100 V cathode bias. After discharge treatments to varied ion-
cathode fluences, tracks formed in CR-39 which consistently corresponded to
138 $\pm$ 21 keV alpha particles emitted from the palladium electrodes. The
track densities for deuterium discharges were often $\sim$100 times above
controls with hydrogen and helium. Currently, there are no known mechanisms to
accelerate ions to these energies within the apparatus. The production of
energetic alpha particles with no source of helium or a means to accelerate
the ions to such high energies indicate a nuclear origin. From particle
trajectory estimates based on track geometries, it was concluded the reactions
originated at the Pd electrodes and not from external sources such as
atmospheric radon or cosmic rays.

## Introduction

This work undertook a systematic study of unexplained energetic charged
particle emission in deuterium-metal experiments[1, 2, 3, 4, 5, 6]. For
example, Chambers et al.[2] observed $\sim$5 MeV particle emission during
350-400 eV deuteron bombardment of titanium or the work done by Cecil et
al.[4] showing the production of 1-10 MeV particles as a DC current was
applied to titanium foils with high concentrations of deuterium. Neither group
reached a firm conclusion on particle type. Chambers et al.’s results
suggested protons, deuterons, or tritons while Cecil et al. suggested 3He or
alphas. As these experiments are derived from interest in “cold fusion” within
a heavy water electrolysis cell with a palladium cathode and platinum anode
[7], it is natural to focus on possible deuteron-deuteron fusion reaction
enhancements due to internuclear many-particle processes in metal-hydrides
leading to Coulomb barrier suppression[8]. However, the 1-10 MeV particle
energies do not correspond to the nuclear byproducts associated with $dd$
reaction pathways [9] and require an alternate explanation.

Once laboratories began reporting other peculiar aspects on deuterium loading
of metals, it was realized $dd$ fusion was too myopic of a view and the field
took on a new name, Low Energy Nuclear Reactions (LENR). Miley et al. found
another surprising feature of LENR wherein Neutron Activation Analysis (NAA)
and Secondary Ion Mass Spectrometry (SIMS) showed transmutation reactions
occur within thin films of nickel[10] and titanium[11] coatings on
microspheres in a packed-bed electrolytic cell. A particularly relevant
hypothesis was the necessity for dense deuteron formations within the
electrode lattice to produce LENR effects [12].

The focus of the present study was then to create dense deuteron formations
using ion bombardment of palladium electrodes within a DC discharge while
monitoring for charged particle nuclear byproducts. It is not intended to
provide a comprehensive history of LENR nor is it attempting to build a
theoretical framework describing LENR phenomena. The reader is cautioned with
the author’s opinion that it is premature to propose a physical mechanism that
governs the phenomena as the relevant parameters of the plasma-material
interaction have yet to be studied, such as ion and excited species
concentrations and energy distributions at the electrode surface along with
possible long-lived metastable states. Further detail on the context of the
research is provided in the author’s Ph.D. thesis[13].

It is important to note that the aforementioned studies of metal-hydride
systems involve both the metal properties and the dynamics of how the hydrogen
enters the metal. In the case of the present work utilizing a DC discharge, an
interplay exists between incident plasma ions on the electrodes, defect
formation, and the mobility and trapping of solute ions in the metal lattice.
Fig. 1 is a diagram that depicts the ion bombardment of a crystalline lattice.
The left portion of Fig. 1 is a palladium-deuteride composed of dilute
deuteron interstitials. Upon continued deuteron implantation, various types of
defect traps are formed along with a high concentration deuteride phase
designated in red.

Figure 1:  Plasma ion implantation and defect formation. (Left) interstitial
deuterons form a dilute phase metal-hydride, $\alpha$-PdDx, with x $<$
$\sim$0.025. Where x is the atomic ratio [D]/[Pd]. (Right) high concentration
phase formed in red, $\beta$-PdDx with x $>$ $\sim$0.6, and deuterium trapped
at defects, e.g., vacancies, dislocations, and voids to form clusters.

Multiple factors were taken into consideration to determine the optimal
charged particle detection method. A time-integrative high detection
efficiency feature was desirable to accumulate counts in the case of low
emission rates in order to increase the signal-to-noise ratio. The detector
must be unaffected by electromagnetic interference due to unsteady plasma
characteristics which was a significant obstacle for Chambers et al.[2] using
Silicon Surface Barrier Detectors (SSBD) and later also found by the author of
this work. The detector must also be insensitive to visible to UV photons from
discharges. Large detection areas with high spatial resolution were required
to investigate the possibility of detecting localized “hot spots” of emission
from distinct deuteron-clusters in the metal electrode. Lastly, the detector
must be able to withstand high-vacuum and a low pressure hydrogen atmosphere.
The Solid-State Nuclear Track Detector (SSNTD) commercially known as CR-39 was
then chosen as the charged particle detector.

Fig. 2 portrays the experimental approach of this research: tracks in CR-39
after a deuterium DC discharge with Pd electrodes that correspond to roughly
100 keV alpha particles. Fig. 2a is an image of the discharge in 10 Torr of D2
operating at 40 mA/cm2 current density. Fig. 2b is an optical image of the
tracks using a reflected light microscope. Fig. 2c shows the depth profile of
a track using a 3D laser scanning confocal microscope, Keyence VK-X1000. Then
Fig. 2d is the 3D image of the track, along with a representation of the
incident particle. A new automated CR-39 analysis technique amassed
considerably more track data than previous studies, e.g.,
$\sim$103tracks/cm2/day for a 1 cm2 chip by Lipson et al.[14] compared to
$\sim$106tracks/cm2/day for a 6.25 cm2 chip in the current work. As helium was
not present in the system and the energies of the alphas exceeded known
acceleration mechanisms in the device, it was concluded that a nuclear
reaction produced the particles. Other causes of tracks were investigated,
such as charge build-up on the CR-39 surface but were inconsistent with the
observed data.

Figure 2: (a) DC discharge with trajectories of emitted particles and CR-39.
(b) optical image of tracks in CR-39. (c) the depth profile of a track along
with an estimated incident angle. (d) 3D image of the track with a
representation of the incident particle.

## Methods

A schematic of the vacuum system and electrode apparatus are shown in Fig. 3.
The chamber could reach below 10-7 Torr. The main chamber is an 8-inch
diameter cross wrapped in heating tape. Bakeouts were done for roughly 12
hours with the temperature set to 325$\degree$C at the center of the main
chamber’s external wall using a K-type thermocouple. The Residual Gas Analyzer
(RGA) was used to verify adsorbed gases were released, such as water partial
pressure below 1 $\times$ 10-9 Torr. A thermal camera showed uneven
temperature profiles of the chamber walls and was the cause of setting the
set-point to 325$\degree$C to ensure the entire 8-inch diameter cross reached
200$\degree$C. The gases were purchased from Airgas, An Air Liquide Company.
The purity for the hydrogen and helium gases was $99.999\%$ while deuterium
was $99.9999\%$. The palladium electrodes were biased by a Glassman High
Voltage EK series 600 W DC power supply. A LabView program was written to
acquire the voltage, current, pressure, and an estimate for the cathode’s ion
fluence in 0.1 ms intervals. The fluence assumes the plasma is localized on
the cathode, which was visually verified and did not take into account
backscatter ions or secondary electron emission.

(a)

(b)

Figure 3: (a) Schematic of vacuum system. (b) Electrode and CR-39 diagram.

The electrode discharge gap in Fig. 3(b) was 5 mm while the active cathode
area was 0.5 cm2. One cause of concern for accurate modeling of the discharge
is the anode grid sometimes deformed and warped which leads to an uneven
electrode gap distance. A 1/8" thick Macor ceramic sheet with a 0.5 cm
diameter circular window was placed over the cathode tab to prevent the
discharge from interacting with the high voltage connection. Along with the
Macor aperture, the electrode setup had the Pd electrodes inside solid alumina
holders for electrical insulation. The cathode holder was joined to a copper
cooling block using high-temperature silver paste. Tap water was used as the
coolant at a flow of 3 L/min. The cathode was formed from palladium bullion
from Valcambi Suisse with a purity of 99.95$\%$ which was rolled to 0.3 mm
thick 1 cm x 0.7 cm tabs. Once the electrode holder was fixed in the system
and baked out, the chamber was pressurized with the gas used for the test
(hydrogen, deuterium, or helium) at 10 Torr and a discharge was struck under
current control at 10 mA with the Pd tab positively biased, 300-400 V, for
surface cleaning. The chamber was then pumped down again to high vacuum for 30
minutes to an hour and then the ion treatment began with the Pd tab biased
negatively as the cathode with the grid anode grounded.

The CR-39 holder was fixed to a linear motion feedthrough. To reduce CR-39
exposure to high temperatures during bakeouts, the holder was moved 13 cm away
from the electrode at the edge of the main chamber where the 8-inch cross
joins to a 2.75 inch cross which held the pirani gauge, electrical
feedthroughs, and mass flow controller as shown in Fig. 3(a). A thermocouple
was placed on the CR-39 holder during a bakeout and it reached a maximum
temperature of $95\degree$C. The possibility of the bakeouts affecting the
CR-39 and degrading the polymer was investigated by placing CR-39 inside the
vacuum chamber in proximity to the external thermocouple which was held at
$150\degree$C for 12 hours. After cooling to room temperature and removal from
the vacuum chamber, the CR-39 piece was irradiated with unattenuated ^241Am
alphas. No noticeable effects were found on surface morphology and track
diameters.

### Solid-State Nuclear Track Detectors

SSNTDs are used as a passive radiation detector in a variety of fields,
ranging from elementary particle physics, fission research, and atmospheric
radon measurements to geology and biology. The poly-allyl diglycol carbonate
polymer (C12H18O7) known as CR-39 is the most popular commercial track
detector. Its ease of sample preparation and low cost are two of the primary
factors in its widespread use. Other advantages are its light weight, lack of
external electronic systems susceptible to noise, ability to be cut and placed
in locations and environments which hinder the use of other detection methods,
and its intrinsic detection efficiency nearing 100$\%$ for charged particles
with normal incidence over a wide energy range. The method can be used in high
or low dose radiation environments and can permanently save the incident
particle’s trajectory with micron spatial resolution [15, 16]. Not only does
it store the particle’s trajectory, but it is also possible to deduce the
particle charge, mass, and energy from the track characteristics given
appropriate calibration standards.

When an energetic particle passes through the polymer, it produces along its
path a region that is more sensitive to chemical etching than the rest of the
bulk. Fig. 4 depicts the stages of charged particle detection in polymer
SSNTDs. The particle deposits energy primarily following the electronic
stopping power in the substrate, which leaves an ionization trail. Bonds break
in the polymer, and radicals and defects form. The open ends of the chains can
then be easily attacked and preferentially etched. After treatment with an
etching solution, e.g., NaOH or KOH, tracks remain as pits in the plastic.

Figure 4: Stages of charged particle detection in CR-39 polymer.

The etched track morphology then provides information as to the type of
charged particle, along with a rudimentary form of energy spectrometry. A
calibration curve of the etched pit diameter versus incident energy is formed
for particles of interest for each batch of CR-39 as the detector’s response
to etching varies based on manufacturing procedures. Most studies using CR-39
for energy spectrometry focus on normal incidence angles, which limits
quantifiable particle tracks [17, 18, 19, 20, 21, 22]. An example of off-
normal tracks from an unattenuated ^241Am source (dominant alpha population at
5.486 MeV) then etched in a 6.25 M solution of NaOH for one hour at $98\pm
1\degree$C is shown in Fig. 5(a). One can see the tracks vary in shapes,
sizes, and contrast. Tracks that are more circular at the surface can be
discriminated, and then the features can be analyzed and correlated to
particle type and energy. Fig. 5b is obtained from a 3D laser confocal
microscope. It shows the depth profiles used for angle of incidence estimates.
The track of interest is annotated in Fig. 5(a), and it is estimated to have
an $80\degree$ angle of incidence with respect to the surface of the CR-39,
see angle [4] in Fig. 5(b).

(a)

(b)

Figure 5: Tracks produced from ^241Am source. Etched in 6.25 M NaOH for one
hour at $98\pm 1\degree$C. (a) optical image using a reflected light
microscope. (b) 3D laser confocal microscope image showing depth profile of
individual track.

### Semi-Automated Imaging Procedure

A novel imaging system was developed to automatically obtain images over large
surface areas of CR-39. The apparatus is described in [23, 24, 13]. The
objective was to autonomously obtain images of an entire CR-39 sample over
large surface areas, e.g., 6.25 cm2. For that purpose, two components were
necessary: an imaging system and a stage capable of moving the sample relative
to the imaging optics. The sample movement was accomplished using an X-Y
plotter stage with an arm extension connected to a microscope slide. A user-
defined path, dependent on the sample dimensions, determines the sample
movement, while the microscope captures images at an optimal focus based on a
Laplacian image filter. The automated stage movement and focusing was
implemented with a python script and an Arduino microcontroller.

Before any experiments, a deleting etch is done to remove background tracks
and surface damage. The deleting etch ensures that any subsequent tracks found
originated during the time between the deleting etch and the following track
etch. The deleting etch solution is 60$\%$ methanol and 40$\%$ 6.25 M NaOH at
$75\pm 5\degree$C for 2 hours, which removes the top $\sim$150 µm of material.
The methanol reduces the track-etch sensitivity and allows for an even etch
profile. After the experiments, etching was done with 6.25 M NaOH at
$98\degree$C for one hour. An example of a CR-39 treatment procedure is shown
in Fig. 6 where, (1) the CR-39 is cut into desirable sizes using a laser
cutter, (2) deleting-etch, (3) the samples are exposed to a radioactive
source, (4) track-etching, and (5) the imaging system.

Figure 6: An example CR-39 experimental procedure.

Once a surface image of the sample is obtained, a machine learning pipeline
was implemented to reduce human error while detecting particle tracks. The
software used was $ilastik$ \- an easy-to-use tool to perform segmentation and
classification during image processing without substantial computational
programming expertise [25]. It includes supervised machine-learning techniques
that rely on the user to classify objects to effectively “train” the software
from examples such as tracks, defects, scratches, dust, etc. The training data
comprised 58 example images of a sample irradiated with ^241Am along with
samples that were left open to atmosphere for 7 days. The samples were then
etched for 1 hour at $98\pm 1\degree$C in 6.25 M NaOH.

The analysis pipeline was tested with samples that were irradiated with the
^241Am source to determine the detection efficiency (the percentage of tracks
correctly identified in an image). The samples were first put through a
deleting etch, irradiated with the ^241Am source, and etched again for tracks.
Thirteen images with $\sim$800 tracks went through the analysis pipeline and
the total standard error compared to manual counting was 2.28$\%$. For
subsequent analysis, this error was increased to 15$\%$ to be conservative.

### Minor Axis Versus Alpha Energy Calibration of CR-39 Using an Am-241 Alpha
Source

A 0.12 µCurie ^241Am unsealed source from Isotope Products Laboratories was
used to calibrate the CR-39 response to alpha particles with an etching
procedure of 1 hr in 6.25 M NaOH at $98\pm 1\degree$C. The resulting trends
and analytical fits of the empirical data are used to compare the tracks from
the plasma experiments. The calibration entails minor axis versus energy,
minor axis versus energy for progressive etchings, and minor axis versus
etching time, while the next section covers the depth of tracks versus energy.
Fig. 7 shows tracks due to alphas from the ^241Am source placed directly on
the CR-39. The figure is annotated with the minor and major axis of a track.
Particles of normal incidence are typically used for calibration, however,
often this is not available as depicted by the elongated tracks due to
particles incident with high angles in Fig. 7. Because of this dependence of
track diameter on incident angle, the track minor axis was chosen as the
calibration parameter since it depends less on the angle than the major axis
[26]. A circularity parameter of 0.91 was chosen to discriminate from incident
angles too far off normal, where circularity is defined as
$Circularity=4\pi(area/perimeter^{2})$. An air gap between the ^241Am source
and CR-39 surface was chosen for energy attenuation of 5.486 MeV alphas. Using
TRIM, the resulting transmitted energies of 100,000 alphas was calculated over
a range of gaps and the data is shown in Table 1.

Figure 7: Tracks due to alphas emitted from the ^241Am source placed directly
on the CR-39. Table 1: Air gaps and corresponding transmitted energies
calculated using TRIM.

Air gap (mm) | Error (mm) |  | Transmitted
---
energy (MeV)
| Standard
---
Deviation (MeV)
0 | 0.005 | 5.49 | 0.05
10.54 | 0.005 | 4.52 | 0.02
20.3 | 0.005 | 3.47 | 0.03
29.74 | 0.005 | 2.25 | 0.05
35.03 | 0.005 | 1.27 | 0.07
37.5 | 0.005 | 0.71 | 0.08
38.31 | 0.005 | 0.52 | 0.08
38.96 | 0.005 | 0.37 | 0.07
39.39 | 0.005 | 0.28 | 0.06
39.9 | 0.005 | 0.19 | 0.05

After irradiation, the samples were etched, imaged, and analyzed. An example
of the track minor axes histogram fit with a biagaussian curve is shown in
Fig. 8. The bigaussian curve fit was used instead of a gaussian curve to
counteract deviations in the distance between the source and the CR-39 surface
and to account for the ^241Am source’s polyenergetic decay scheme, i.e., 5.486
MeV (85.2$\%$) and 5.443 MeV (12.8$\%$). Examples of tracks for each energy is
shown in Fig. 9. The change in contrast is noticeable as the energy decreases
and the tracks become shallower.

Figure 8: Bigaussian peak fit of minor axes for tracks corresponding to
$4.52\pm 0.02$ MeV alphas.

(a)

(b)

(c)

(d)

(e)

(f)

(g)

(h)

(i)

(j)

Figure 9: Tracks due to alphas with energy (a) 5.49 MeV, (b) 4.52 MeV, (c)
3.47 MeV, (d) 2.25 MeV, (e) 1.27 MeV, (f) 0.71 MeV, (g) 0.52 MeV, (h) 0.37
MeV, (i) 0.28 MeV, and (j) 0.19 MeV.

The minor axis data is then used to make the final minor axis versus incident
energy for alpha particles shown in Fig. 10. This data is analogous to what
was done by Duan et al. in [27] for monoenergetic protons of (a) 20 keV, (b)
40 keV, (c) 60 keV, (d) 80 keV, (e) 100 keV, (f) 220 keV, (g) 320 keV, (h) 420
keV, (i) 520 keV, (j) 620 keV, (k) 820 keV, and (l) 1200 keV then etched in 6
M NaOH at $70\degree$C for 9 hours. It was noted that the optical limit for
observing tracks from protons for their etching procedure and optical imaging
was $\sim$20 keV. The optical limit of alpha tracks for the imaging system
used in this work with a resolving power of 2.58 µm was then 31 keV.

Figure 10: Minor axis versus energy for ^241Am alphas attenuated with air gaps
of varying distance. Exponential decay fit for the lowest five energy values.

### Progressive Etching of the Five Lowest Energy Tracks

As the tracks produced from the discharge experiments had diameters $\sim$8 µm
and low contrast indicative of a shallow track, the lowest five energy tracks
were of focus for progressive etchings. Progressive etching of tracks is
another tool in examining track characteristics and to determine the particle
type. Examples of the tracks due to 0.37 MeV alphas as they progressed are
shown in Fig. 11. The minor axis versus energy was plotted for each etching
interval and then fit to an exponential decay - all plotted together in Fig.
12a. The same exponential decay shape was found for all etching interval data
sets. Next, the minor axis versus etching time for each energy was explored,
compiled in Fig. 12b. The minor axis was found to increase linearly with
etching time, which means the latent damage in the polymer was still being
preferentially etched compared to the bulk. The linear relationship between
track minor axis and etching time was also found in [28]. Last, the linear
slope for each energy interval was fit to an exponential decay as another
comparison measure - Fig. 12c.

(a)

(b)

(c)

(d)

(e)

Figure 11: Tracks due to 0.37 MeV alphas after etching in 6.25 M NaOH at
$98\pm 1\degree$C for (a) 1 hr, (b) 1 hr + 1 hr, (c) 1 hr + 1 hr + 1 hr, (d) 1
hr + 1 hr + 1 hr + 1 hr, and (e) 1 hr + 1 hr + 1 hr + 1 hr + 1 hr.

(a)

(b)

(c)

Figure 12: (a) Minor axis versus energy for progressive etches. The error bars
for the energy were removed for ease of viewing. (b) Minor axis versus etching
time for all energies. (c) Slope of the minor axis versus energy linear fits
for all energies.

### 3D Laser Scanning Microscope - Track Depths and Minor Axis Diameters

This section covers the use of a Keyence 3D Laser Scanning Microscope -
VK-X1000. It was used to investigate the track depths for the five lowest
energies, again 0.71 Mev, 0.52 MeV, 0.37 MeV, 0.28 MeV, and 0.19 MeV. The
measurements were done manually which limits the selection of data to roughly
30 tracks for each energy interval. Due to the small sample size, the data was
fit to a broad normal distribution instead of the bigaussian peak fit used in
the previous automated analysis pipeline.

Examples of chosen track depth profiles from 0.52 MeV and 0.28 MeV alphas are
shown in Fig. 13. The track depth versus energy along with measured diameter
versus energy are then shown in Fig. 14. In this energy regime, incident
angle, and etching procedure, the diameter is synonymous with the minor axis
of the track. The diameter versus energy data for the manual Keyence
measurements aligns well with the automated analysis pipeline - Fig. 14c.

(a)

(b)

Figure 13: Examples of tracks and their depth profiles taken using the Keyence
VK-X1000 for alphas of energy: (a) 0.52 MeV and (b) 0.28 MeV.

(a)

(b)

(c)

Figure 14: Curve fits for (a) track diameter versus energy and (b) track depth
versus energy. (c) Track minor axis versus energy for the automated analysis
pipeline and Keyence VK-X1000 data.

## Experimental Results

### Comparison of hydrogen and helium controls and deuterium track densities

Fig. 15 shows an example of tracks after discharges with deuterium, hydrogen,
and helium. The parameters for (a), (b), and (c) were 10 Torr, 5 mm electrode
gap distance, -500 $\pm$ 100 V, 40 $\pm$ 0.3 mA/cm2, 5 $\times$ 1021 ions/cm2
cathode fluence, and z = 5 cm between the cathode and CR-39. Fig. 15d went to
a fluence of $5\times 10^{22}$ ions/cm2 and shows how the detector eventually
saturates with tracks.

(a)

(b)

(c)

(d)

Figure 15: Example of tracks after discharges with palladium electrodes. (a)
and (d) deuterium, (b) hydrogen, and (c) helium.

As the CR-39 chips often had different active areas due to using attenuation
foils over sections of the chip, the surface images were discretized to 0.25 x
0.25 mm2 bins and the number of tracks per bin was used to produce track
densities for comparison. The track density data was normalized to the total
time of exposure from the deleting etch up to the track etch to combat the
effect of track densities increasing as their exposure time increased. For
example, if it took three days between the deleting etch and track etch, then
all the track density data is normalized by three days. The maximum track
density normalized to the total exposure time of the detector was chosen as a
criterion for comparison between the controls and deuterium tests. The
normalized maximum track density versus cathode ion fluence is shown in Fig.
16. The data labels are designated as Pd$\\_$X.Y where X is the electrode
number of an unused As-Received Pd sample and Y is the number of experiments
conducted with the same electrode. For example, Pd$\\_$30.14 is the 30th Pd
electrode that was used in 14 separate plasma experiments. Note that Pd$\\_$30
was tested with deuterium and subsequently with hydrogen, which produced
decreased track densities.

Figure 16: Normalized maximum track density versus fluence for deuterium,
hydrogen, and helium discharges at 10 Torr.

The mean normalized maximum track densities for hydrogen and helium were
$102\pm 23$ tracks/mm2/day and $70\pm 27$ tracks/mm2/day, respectively. The
slight discrepancy could be due to the presence of deuterium in the hydrogen
supply. Table 2 then shows the ten highest normalized maximum track densities
and their corresponding electrodes, current densities, and fluences. The mean
normalized maximum track density for these was $(8.41\pm 2.27)\times 10^{3}$
tracks/mm2/day. The highest track density of $1.01\times 10^{4}$
tracks/mm2/day was 129 times above the control tests.

Table 2: Ten highest normalized maximum track densities and their
corresponding electrode, current density, and fluence.

Electrode |  | Current Density
---
(mA/cm2)
| Fluence
---
(Ions/cm2)
| Normalized Track Density
---
(Tracks/(day x mm2))
Pd$\\_$30.17 | 10 | $5.0\times 10^{21}$ | $1.1\times 10^{4}$
Pd$\\_$32.6 | 80 | $1.1\times 10^{22}$ | $1.0\times 10^{4}$
Pd$\\_$32.3 | 40 | $5.1\times 10^{21}$ | $9.4\times 10^{3}$
Pd$\\_$32.8 | 80 | $5.0\times 10^{22}$ | $9.4\times 10^{3}$
Pd$\\_$33.2 | 40 | $1.7\times 10^{22}$ | $9.3\times 10^{3}$
Pd$\\_$30.18 | 160 | $3.8\times 10^{22}$ | $9.1\times 10^{3}$
Pd$\\_$33.2 | 40 | $1.7\times 10^{22}$ | $8.8\times 10^{3}$
Pd$\\_$32.4 | 80 | $5.0\times 10^{21}$ | $8.2\times 10^{3}$
Pd$\\_$30.14 | 40 | $5.0\times 10^{21}$ | $4.7\times 10^{3}$
Pd$\\_$32.3 | 40 | $5.1\times 10^{21}$ | $4.0\times 10^{3}$

### Plasma Track Diameters and Energy Estimates

Tracks produced from a discharge were then compared to the ^241Am
calibrations. The discharge parameters were 10 Torr D2, -540 $\pm$ 43 V, 40
$\pm$ 0.2 mA/cm2, $1.74\times 10^{22}$ ions/cm2 cathode fluence, and z = 5 cm
between the cathode and CR-39. Examples of tracks are shown in Fig. 17. The
bigaussian peak fit for the track minor axes is shown in Fig. 18. The minor
axis peak was $8.49\pm 0.90$ µm. Using the ^241Am calibration curve, the
incident energy was $118\pm 17$ keV.

(a)

(b)

(c)

(d)

Figure 17: Additional examples of tracks from a deuterium discharge with Pd
electrodes. Figure 18: Bigaussian peak fit of the minor axis histogram data.

The incident energy is for a particle hitting the CR-39 surface. The particle
first has to traverse 5 cm of 10 Torr D2 gas, resulting in energy attenuation.
The energy loss was calculated using TRIM with a deuterium gas density of
$2.203\times 10^{-6}$ gram/cm3 and a compound correction of 0.976 from the
compound dictionary for mylar[29]. The energy loss then corresponds to the
emitted energy required for the particle to have the measured incident energy.
The estimated emitted energy was then $139\pm 19$ keV.

### Plasma Track Diameters Progressive Etching

Following a deuterium discharge, a detector chip ($\\#$00143) was processed
using the same protocol as the five lowest energy ^241Am calibration
detectors. Examples of tracks as they progressed with subsequent etchings are
shown in Fig. 19. Table 3 shows each etching interval’s measured minor axis,
along with the corresponding incident energy. The mean incident energy for all
the etching intervals was $116\pm 8.9$ keV. The minor axis versus etching time
for the active chip and the calibration data are shown in Fig. 20. The slope
for the active chip progressive etch was $2.46\pm 0.06$ and using the slope
versus energy calibration curve for ^241Am shown in Fig. 12c corresponds to
$106\pm 3$ keV.

(a)

(b)

(c)

(d)

(e)

Figure 19: Examples of tracks in 00143 for each progressive etch. (a) 1 hr,
(b) 1 hr + 1 hr, (c) 1 hr + 1 hr + 1 hr, (d) 1 hr + 1 hr + 1 hr + 1 hr, (e) 1
hr + 1 hr + 1 hr + 1 hr + 1 hr. Table 3: Minor axis and incident energy at
each etch interval for 00143.

| Etching Time
---
(hrs)
| Minor Axis
---
(um)
| Incident Energy
---
(keV)
1 | $8.81\pm 0.88$ | $125\pm 16.1$
2 | $11.5\pm 1.36$ | $91.9\pm 20.8$
3 | $13.8\pm 1.72$ | $127\pm 18.9$
4 | $15.9\pm 2.34$ | $99\pm 24.2$
5 | $18.8\pm 2.48$ | $137\pm 18.3$

Figure 20: Progressive etch minor axis versus etching time with the ^241Am
standards.

The progressive etchings provide additional support that the incident
particles were roughly 100 keV alphas. How a track progresses depends on the
damage profile in the material, i.e., the amount of energy transferred from
the penetrating particle that breaks bonds in the CR-39 polymer. The
progression of a track cone is referred to as the track-etch rate and is
covered in more detail in [15, 16, 19]. The plasma track-etch rates followed
the same linear trend as the calibration tracks from ^241Am, which further
supports a charged particle origin.

### Plasma Track Depths

This section follows the same procedure to measure the track depth and minor
axis diameter using the Keyence VK-X1000 3D laser scanning microscope as was
done with the ^241Am calibration detectors. As with the ^241Am data, these
measurements were done manually which limited the data to $\sim$200 tracks.
The mean track depth was $0.40\pm 0.17$ µm with a mean minor axis diameter of
$7.60\pm 1.41$ µm. The resulting incident energy from the diameter and depth
measurements were $93\pm 26$ keV and $99.8\pm 17.3$ keV. Taking into account
the attenuation effects of 5 cm of deuterium gas at 10 Torr, the diameter and
depth measurements suggested that the emitted energies from the cathode were
$109\pm 29$ keV and $117\pm 19$ keV, respectively. An example of a track depth
profile is shown in Fig. 21.

(a)

(b)

Figure 21: Examples of tracks and their depth profiles taken using the Keyence
VK-X1000.

As with the track progressions, the depth of the plasma tracks indicate
$\sim$100 keV alpha particles. It seems unlikely that an alternative damage
mechanism would produce tracks that followed the same track-etch rate and have
the same depth profile as the proposed alpha particle. Further support for an
energetic charged particle origin is presented in the following sections.

### Incident Particle Trajectories

The location of the tracks on the detector coupled with their depth profiles
allows for an estimation of the trajectories of the incident particles that
produced the tracks. A plasma discharge experiment was done over a range of
pressures from 1 to 10 Torr and current densities from 5 to 60 mA/cm2, to a
fluence of $1.41\times 10^{23}$ ions/cm2, as this was in the initial stages of
the project and the parameters that led to tracks were still unknown. It was
this test that significantly increased confidence the shallow tracks were due
to heavy charged particles impacting the detector. The procedure entailed
using the 3D laser scanning microscope Keyence VK-X1000 to extract depth
profiles and from those an angle of incidence was estimated. The angles of
incidence in combination with the location of each measured track on the
detector allowed for the representation of trajectories.

Two detectors were analyzed with the 3D microscope after the etching
procedure. Three y-axis locations were scanned across each sample, as depicted
in Fig. 22. An example of the track depth profile and angle of incidence
estimate is shown in Fig. 23. The incident angles were estimated by first
forming an angle between the track walls, angle [1] in Fig. 23b, which was
$140\degree$. Angle [1] was then bifurcated into two equal angles, [2] and
[3], $70\degree$. Then angle [4] is measured with respect to the surface of
the CR-39 to produce the incidence angle which was $84\degree$. A source of
error results from the tracks being in the transition phase where they are not
perfectly conical and have rounded sides which obfuscates the proper angle
formed between the track walls.

Figure 22: Diagram of samples analyzed for track angles of incidence.

(a)

Figure 23: (a) Example of track used for trajectory estimate. (b) Estimating
incident angles by bifurcating the track cone.

The coordinates of the tracks, along with the angle of incidence, were then
used to project the trajectories. An example of the trajectory plot is shown
in Fig. 24a. The 2D projection of the trajectories for a cross-section at 5 cm
from the detector surface is shown in Fig. 24b. It is apparent that the
particles point toward a common point of origin at the cathode, which removes
the possibility of cosmic rays coming from outside the chamber causing the
tracks. The trajectories also remove the possibility that the particles
originated from elsewhere in the chamber, e.g., the chamber walls. Hence, the
environment, and subsequent mechanism, that produced the alphas is confined to
the plasma and electrodes.

(a)

(b)

Figure 24: (a) 3D trajectories for particles that produced tracks in CR-39.
(b) Cross-section of trajectories at z = 5 cm from the detector surface which
is where the cathode is located.

### Au Deposited on CR-39

This section covers the deposition of gold on the CR-39 to investigate whether
a type of charge buildup or plasma radical interaction caused the tracks in
the plasma experiments. This test is also a method for energy spectroscopy.
Depending on the thickness of gold, bounds are set on the minimum and maximum
energies for an alpha particle to traverse the gold and retain enough energy
to produce a track or be absorbed in the gold. An AJA ORION 3 sputter system
was used to deposit 100 nm and 500 nm layers of gold on two detector chips.
During the test, uncovered CR-39 was also present. The plasma parameters were
10 Torr D2, 40 mA/cm2, to a fluence of $5\times 10^{21}$ ions/cm2. A concern
was that the gold layer would prevent the NaOH etching solution from
interacting with the CR-39 and forming tracks. Preliminary Au deposited
samples irradiated with unattenuated ^241Am were observed during the etching
procedure and it was found that the gold layer quickly detached from the
detector’s surface once it was submerged in the $98\degree$C 6.25 M NaOH and
did not significantly hinder the etching process.

Despite the 100 nm Au layer, elevated track densities were still present. The
maximum track density was $3.52\times 10^{3}$ tracks/mm2/day. An example of
tracks is shown in Fig. 25a. The track minor axis from a bigaussian peak fit
of the histogram was $7.6\pm 0.6$ µm. This corresponds to $102\pm 11.1$ keV
incident alphas on the CR-39 surface after transmission through the Au layer.
The uncovered CR-39 chip had tracks with a minor axis of $8.57\pm 1.09$ µm
which corresponds to $122\pm 20$ keV incident alphas. A 122 keV alpha
transmitted through 100 nm of gold has its energy attenuated to $88.8\pm 6.9$
keV which is lower than the corresponding energies for the measured track
minor axes. The depth of the gold layer was not measured, so it is possible
less was deposited than desired. Or perhaps the Au deposition process leads to
a type of sensitization of the CR-39 polymer that lowers the energy thresholds
for track formation and hence leads to larger track sizes. Further studies are
needed to determine how much of an impact the Au layer has on the etch rates.
Regardless, the 100 nm Au layer removes the possibility that a charge build-up
effect led to the tracks. The results also provide further evidence that a
chemical reaction between plasma ions and the CR-39 does not cause the tracks.
Supplemental evidence of this is from a test where the CR-39 was placed on the
electrode holder, which produced no increased track densities.

(a)

(b)

Figure 25: Tracks in the detector with (a) 100 nm Au and (b) 500 nm Au.

A 122 keV incident alpha has a maximum electronic stopping power in CR-39 of
17 eV/Å. The electronic stopping power in CR-39 for protons, alphas,
deuterons, 3He+, alphas, and Pd+ ions are shown with a logarithmic y-axis in
Fig. 26. The data were produced using TRIM software (Transport of Ions in
Matter) with a 0.957 compound correction [29]. Only 3He+ and Pd+ ions can
produce a similar value in CR-39. Hence, protons and deuterons were removed
from possible ions that produced the $8.57\pm 1.09$ µm diameter tracks. The
electronic stopping power translates to the ionization of the medium by
incident ions and recoils, which is the latent damage trail that produces
tracks after etching [16, 30, 15]. The 122 keV alpha then has a maximum
ionization of 16.7 eV/Å in CR-39, which is lower than the electronic stopping
power, as energy loss due to phonons and high energy recoil electrons do not
contribute to the latent damage trail. To obtain similar incident ion
ionization values, 3He+ requires an energy of 92 keV and 5.5 keV for Pd+. Pd
ions were included to investigate the possibility that the tracks were due to
sputtered Pd from the electrodes. However, a 5.5 keV Pd+ ion has an average
range of only 26 Å into Au. Therefore, even if there was a mechanism that
accelerated Pd+ ions to such energies, Pd+ cannot be the species that produce
the $7.6\pm 6$ µm diameter tracks in the CR-39 that was deposited with Au.

Figure 26: Electronic stopping power of protons, deuterons, 3He, alphas, and
Pd ions in CR-39 using a 0.957 compound correction.

With a 500 nm Au layer, the maximum track density dropped significantly to
63.5 tracks/mm2/day. An example of the detector’s surface with tracks still
present is shown in Fig. 25b. Using TRIM again, the maximum incident energy
for an alpha particle to not produce a track with a 500 nm Au layer is 135
keV. The mean penetration distance of a 122 keV alpha into Au is $273\pm 105$
Å. Therefore, the 500 nm Au layer prevents alphas from reaching the CR-39 to
produce a track. The maximum track density is within the range for the helium
and hydrogen control tests, i.e., $78.5\pm 37.3$ tracks/mm2/day.

The Au deposited layer studies are consistent with $\sim$100 keV alpha
particles incident on the detectors. The 100 nm Au tests not only showed that
the energy transfer of the track producing particle is consistent with an
alpha, but also removed multiple possibilities for what may have caused the
tracks. First, static charge build-up on the surface of the dielectric CR-39
would no longer take place with the conductive Au layer, hence it is not what
caused the tracks. Second, the gold layer prevents plasma radicals from
interacting with the CR-39 surface and removes the possibility that the tracks
were due to a plasma-material-interaction effect. Third, energetic Pd+ ions
would be unable to traverse the 100 nm Au layer which removes the possibility
of sputtered Pd atoms from the electrodes producing the tracks. Last, the
energy transfer of protons and deuterons is inconsistent with those particles
being capable of producing tracks after attenuation through the gold. As for
the 500 nm Au layer, it drastically reduced the track density from $3.52\times
10^{3}$ tracks/mm2/day for 100 nm Au to 63.5 tracks/mm2/day. The reduction in
track density also supports $\sim$100 keV incident alphas, as they are unable
to penetrate the increased thickness.

## Discussion

A novel automated analytical tool for CR-39 track detection was developed to
investigate the emission of heavy energetic charged particles from a deuterium
DC discharge with palladium electrodes. Extensive CR-39 detector experiments
established the production of tracks that consistently corresponded to low
energy alphas ($\sim$100 keV) emitted from the palladium electrodes. As helium
is not present during the discharge, the emission of alpha particles indicates
a nuclear process. However, the low energy alphas are in striking contrast to
the work by Lipson et al. which used a pulsed deuterium discharge and CR-39 to
monitor $dd$ fusion charged particle byproducts, e.g., 3 MeV protons and 1 MeV
tritons [14]. A reason Lipson et al.[14] did not observe lower energy particle
tracks is due to a 11 µm thick Al foil placed in front of the CR-39, which
would prevent the particles from reaching the detector. The Al foil was
included to protect the detector from plasma interactions and sputtered
electrode material. In the current work, the concern of plasma-CR-39
interaction leading to spurious results was addressed by placing the detectors
farther from the discharge, tests with deposited gold on the detector surface,
and comparisons with discharges using hydrogen and helium which showed no
effect.

It is possible a particle other than an alpha, but with similar stopping power
in CR-39, $\sim$16.7 eV/Å, was incident on the detectors. However, the gold
deposition tests, progressive etchings, and track depth trends strongly
suggest alpha emission. Further evidence that these tracks originated from the
discharge electrodes came from using a Keyence VK-X1000 3D laser scanning
microscope. The 3D microscopy technique allowed estimation of the incident
particle trajectories. It was found that the tracks converged in proximity to
the electrode holder and not from outside the chamber, which excludes cosmic
rays or atmospheric radioisotopes (radon) as the source of the observed
tracks, and limits the reaction site to the electrodes.

Some future research avenues are now briefly discussed. First, it is desirable
to redo CR-39 calibrations while the chips are within a hydrogen environment
similar to the experiments to account for possible hydrogen sensitization of
the CR-39. To that effect, a differentially pumped duoplasmatron ion source is
under construction to calibrate CR-39 with helium ions below 100 keV.
Characterizing the ion source with a light-insensitive Passivated Implanted
Planar Silicon (PIPS) detector then allows for more precise and accurate
knowledge of the ion energy distributions incident on the CR-39. Next, the new
calibration chips will be processed in an improved etching apparatus with
$\pm$0.1$\degree$C temperature stability to reduce variation in track
diameters and increase the energy spectrometry resolution.

Work progressed for some time on implementing a diamond detector to complement
the CR-39 data with prompt particle spectroscopy. However, the geometry and
pressure of the system often leads to an erratic discharge and significant
Electromagnetic Interference (EMI) prevented the diamond detector’s use within
the chamber. Characterizing the high-frequency I-V characteristics of the
discharge then allows for the design of EMI filters to implement the prompt
particle detectors and to determine whether components of the EMI are
correlated to the alpha emission.

Optical Emission Spectroscopy (OES) is also underway for a non-invasive plasma
diagnostic technique. The use of a Langmuir probe was undesirable due to the
small gap distance of the electrodes making it likely the probe would
influence the discharge characteristics leading to erroneous conclusions. OES
would then allow for the investigation of long lived metastable states that
might lead to a type of energy step-up process for such high kinetic energy
particles to exist in a low voltage discharge. Regardless of this conjecture,
it seems natural to investigate the emission of other forms of low energy
radiation such as soft x-rays as done by Miley et al.[31] during DC glow
discharges with Pd and Ti cathodes or low energy beta emission similar to
fracto-emission[32]. Once further diagnostics are included, relationships
between alpha production and system variables will be investigated, such as
current density, high-frequency perturbations of the voltage, pressure, gap
distance, gas mixtures, other electrode materials, and the effects of pre-
discharge hydride-cycling of the electrodes for increased initial defect
concentrations. Lastly, a subsequent paper will detail the cathode surface
morphology and x-ray diffraction for the study of palladium-deuteride phases
present after the deuterium discharge.

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## Acknowledgements

The authors acknowledge the use of facilities and instrumentation at the
Materials Research Laboratory Central Research Facilities, University of
Illinois, partially supported by NSF through the University of Illinois
Materials Research Science and Engineering Center DMR-2309037.

## Author contributions statement

E.P.Z. and G.H.M. conceived of the experiments. E.P.Z. then built the
apparatuses described, conducted the experiments, and analyzed the results.
The author’s then discussed results. All authors reviewed the manuscript.

## Additional information

Competing interests This work was partially funded by NPL Associates, Inc and
Industrial Heat, LLC. Neither organization directed the research or the
interpretation of results.