text:
Just Notice
By: Meilan Solly
LEESBURG, VA- Take an empty classroom at Tuscarora, or any other high school in America. Fill it with students: some Hispanic, some Asian, some Indian or African American, and a majority Caucasian. The end result is a perfectly commonplace, yet somehow still complex combination that can sometimes lead to conflict. As Melina Minhas, freshman, puts it, "Anywhere you find people, you'll find racism."
So, here's an essential question: what IS racism? According to Sam Brazer, freshman, "When someone is racist, they mean what they say." Freshman Dora Ramos' thoughts are that racism happens when people make fun of someone based on their skin color. Brielle Davis, a junior at Loudoun County High School, adds that racism can happen when someone is treated "differently, in any way, good or bad, because of their race." The Merriam-Webster official definition of racism is "a belief that race is the primary determinant of human traits and capacities and that racial differences produce an inherent superiority of a particular race."
It's not easy to pinpoint the exact spot where racism originates, but hate, beliefs instilled at an early age by parents or other authority figures, or people simply disliking each other for no apparent reason are good places to begin. Minhas says, "[Racism] usually starts in a conflict of some sort, and someone just throws in a racist comment to put the other person down." Brazer thinks racism begins with hate. "When one person hates someone else, they make things up about that person or warp things that they do or say," he elaborates.
Some students believe that at Tuscarora, racism is "pretty much everywhere. [It's] in classes, the halls, lunch [room], and even the sports field," Sam Brazer explains. This
situation, however, is not unique to Tuscarora. Brielle Davis says she sees racism "everywhere" at Loudoun County High School. Racism is represented with jokes, snide comments, or maybe even violence. It affects many students, regardless of their race. Dora Ramos says it affects her because she has to witness it. Davis has people telling her she "'act[s] white' all the time," and Brazer says it affects his personality because of what he says. "I will make comments about my own race and some might see that as racism, but I only do it to [joke] around," he clarifies.
Mrs. Davis, a Geometry and Algebra II teacher, says that she's not aware of any instances of racism in her classroom, and Mr. Bednarski, a Pre-AP and AP World History teacher, agrees that it's not a big problem in his classroom. "Occasionally comments are made," he says. There is also stereotyping walking "pretty close to being racially insensitive," but "anything which has caused disruption or interference? No." As for how racism is handled in his classroom, Mr. Bednarski says he will "try to discuss [the incident] directly at the time and after class individually." Mrs. Davis handles racism by not even allowing it to begin. Students basically offer the same consensus about teachers' reactions to racism. "Teachers will usually scold the kid and make them feel bad or embarrassed," Brazer says. Melina Minhas, however, thinks that "teachers are more laid back about it because they hear it every day, so it's pretty normal." One gray area of racism is jokes. Teenagers make jokes about different races all the time. Accent imitations are also popular. However, Ramos, Brazer, Davis, and Minhas all agree that sometimes the perpetrators of these jokes, "are just trying to attract attention from everybody," says Minhas, rather than trying to be hurtful. Brazer says he makes a lot of jokes about
his own race, but he doesn't actually mean any of them. However, he does add that "this contributes to racism because the people who believe the comments made while joking around then repeat what others say" but they aren't joking. Brazer also says, "Adults will stop people who are being racist, but kids will laugh because they are afraid of being made fun of too."
As Davis puts it, "[Students who make racist jokes] should know that what they say may not be hurtful to them, but it can be for another person. It contributes to real racism because it's judging people by the color of their skin." Racism has a long, checkered history. Martin Luther King Jr., Plessy v. Ferguson, and Brown v. Board of Education are all names associated with the fight against racism. Every day more changes are being made to the way racism is born, handled, and destroyed. When Mrs. Davis was in high school, racism was much more accepted. It was "just the way things were." She lived in an area which was much less diverse than Loudoun County, so there was "less tolerance of different races." Mr. Bednarski thinks racism today is very different from the racism of his high school years. "[There's] increased tolerance and acceptance of diversity," he says. Mr. Bednarski acknowledges that there are still racist people, but he thinks people "as a whole"
tolerance has "increased across the board." Now that the effects of racism have been observed, it's time to look at ways to overcome racism once and for all. In schools specifically, Brielle Davis thinks a good way to help control racism would be to "make it a point to punish kids who make racist jokes." Minhas agrees, saying, "I would definitely have teachers give the students punishments for racist jokes right on the spot, because the more you wait to give the punishment, the more the student will think that racism is not that big of a deal." Sam Brazer's change would be to educate students about the effects of racism. As for racism's decline in not only schools, but all other aspects of life, it seems there is a glimmer of hope. It's easy to see that racism has already started to wither a bit since Mr. Bednarski's and Mrs. Davis' high school days. With all the programs working to fight racism, by the time Tuscarora's current students reach adulthood, racism may be gone for good. As for each student individually, there are many ways to help stop racism. Think before you speak, try not to judge someone before you know them, and, of course, no more racist jokes.


When I arrived in Seattle in 2011, residents hereabouts still debated whether there was such a thing as the Seattle Freeze, the notion that Seattleites aren't all that friendly to newcomers. In my view the Seattle Freeze is quite real and has been around for a long time (I found one veiled reference to it in a 1941 book). Long-time visitors will recall I frequently wrote about the concept (just enter the term into the adjoining search box and hit enter).
Why not so much recently? It's become accepted as fact, not opinion, by such esteemed organs of local public opinion as The Seattle Times and now has its own Wikipedia entry. Piling on, perhaps, some out-of-town publications have put Seattle on lists of America's snobbiest cities.
There is also an enduring quirkiness. More dogs than children (by a 3-to-2 ratio) Dogs allowed to ride public buses. Enthusiastic TV weather forecasts of brief sun breaks. Car washes that remain open in the rain. An utter disdain for umbrellas and raincoats. The coffee culture. A counter-culture neighborhood named after a war criminal. And, of course, legal pot.
But this all competes with bad drivers, careless pedestrians, dreadful signage, dodgy charities (too numerous to link here, but click on the nearby category cloud box for "Seattle charities"), a local landscape prone to calamity and that great creator of local heartbreak, the Seattle Mariners.
For most of its history under gringo rule, Seattle was one of the most segregated, racist cities in the North. It continues to be one of the country's whitest big cities.
In case you wonder (or even hope), in leaving Seattle I'm not exiting the blogging game. I obtained the domain name NewToLasVegas.com . It is up now. I hope you'll follow along. I intend to keep NewToSeattle.com running for awhile. Its archive–especially my numerous commentaries on dodgy charities that solicit nationally–draws a lot of traffic. I'll continue to respond to comments.
Just found your blog while checking on the US Armed Forces Association after being asked for a donation . I've decided not to contribute, even though my two oldest sons served 25+ years in three branches of the military. It will be better for me to work for and contribute to Operation Home Front and the Wounded Warriors group.
RE: your Seattle comments: I was born in West Seattle during WW 2 and have seen tremendous changes there, although every time I'm downtown I see another hole in the ground, which is sad. No one is putting up buildings similar to the older ones, with walruses, shells, etc. and the tall clock that stood on 3rd. Ave. downtown for many years (75 ?) is now gone. I'm sure you know what the new buildings are compared to — I'm not going to say it online! Anyway ,I hope Las Vegas becomes a good place for you.
West Seattle strikes me as the most authentic, unchanged and unpretentious part of Seattle. I refereed youth soccer on West Seattle fields for five years and found the area to be very interesting.
Well, we "followed" you to Seattle and will now follow you to Las Vegas (which I will count as one of the rare benefits of communication technology). One could argue that geography is irrelevant to those of us who stay in touch primarily in the virtual world, but we'll still indulge ourselves in a little melancholy, Bill. We picture you in Seattle muck and drear, and reflected in those infrequent, crystalline, sparkling days on the Sound, and (affectionately) enjoy your poking holes in the carefully woven fabric of pretense that Seattle wraps itself in. You have your work cut out for you, Mr. Barrett, finding irony in a city that so shamelessly wears its failings proudly on it's sleeve.
You're the man for the job, though: best wishes for the happiest of landings.
Good luck, Bill! I have forwarded some of your blog posts to a Seattle friend.He will miss you, too.
I enjoyed reading your blog. Good Luck in Vegas.
Thanks. You can follow me on NewToLasVegas.com. First post went up today.


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redpajama_set_name: RedPajamaCommonCrawl


text:
\section{Introduction}
   Exoplanet surveys are beginning to yield large amounts of observational data on low-mass exoplanets \citep{Batalha_2013, Mayor_2014, Dressing_Charbonneau_2015}. There is substantial evidence from planet formation theory and condensation models suggesting that particular chemical species present in a host star's photosphere are reflected in the composition of those planets \citep{Bond_2010, DelgadoMena_2010, Thiabaud_2015, Dorn_2017_1, Dorn_2017_2, Santos_2017}. The correlation exists in the solar system where there is good agreement between the chemical abundance ratios in the solar photosphere and primitive meteorites, the latter is often used to ascribe rocky planet bulk compositions \citep{Lodders_2003}.

   The solar neighbourhood in our galaxy is composed of three distinct stellar populations, the thin disc (of which the Sun is a part), the thick disc, and the halo \citep{Buser_2000, Nissen_2004}. These three galactic substructures can be separated according to their kinematics \citep{Reddy_2006, Bensby_2014}, age \citep{Bensby_2005, Yoachim_2008}, chemical abundances \citep{Navarro_2011, Adibekyan_2012_1}, or a combination of the three \citep{Fuhrmann_1998, Haywood_2013}.

   From \citet{Santos_2017} and \citet{Cabral_2019}, the three stellar populations demonstrate variability in the chemical abundance ratios that are expected to form the bulk composition of rocky planets. In these studies, observations from a HARPS sample from \citet{Adibekyan_2012_1} are used by \citet{Santos_2017}. Separately, the Besançon stellar population synthesis model \citep{Lagarde_2017} is used by \citet{Cabral_2019} to show the expected different iron and water mass fractions between galactic stellar populations.

   Within this context, we investigate whether we can differentiate the mass-radius (\textit{M - R}) relations for solid planets between the three galactic stellar populations using stellar chemical compositions. The paper is organized as follows; in Sect.~\ref{sec:data}, we present the stellar sample and the kinematic separation method. In Sect.~\ref{sec:methods}, we discuss the two internal structure models used to generate synthetic solid planets. In this same section, we also explain the analytic stoichiometric model used to determine the planetary composition inputs to the models. In Sect.~\ref{sec:results}, we present our results. In Sect.~\ref{sec:discussion}, we discuss and place the results in perspective of possible observations.
\section{Data}
\label{sec:data}
   The stellar sample used for this work comes from the Hypatia catalog, a resource containing multiple literature sources of precise stellar abundances matched to \textit{Gaia} or \citet{Anderson_2012} stellar properties \citep{Hinkel_2014, Hinkel_2016}. From the catalog, we limit the sample to F, G, and K-type stars, with a \textit{V} mag < 11, and a log \textit{g} > 3.5 dex. We limit the stellar sample to simulate a potential PLATO sample that could be subject to high precision planetary mass-radius measurements \citep{Rauer_2014, Miglio_2017}. For this study, we selected stars with complete data on their absolute stellar abundances of Fe, Si, Mg, and O, as well as having an Mg/Si ratio as $0.7\leq$Mg/Si$\leq1.7$ due to model parametrization constraints (see Sect.~\ref{sec:modelb}). Throughout this paper, Fe/Si = $\mathrm{N}_{\mathrm{Fe}}/\mathrm{N}_{\mathrm{Si}}$ and Mg/Si =  $\mathrm{N}_{\mathrm{Mg}}/\mathrm{N}_{\mathrm{Si}}$, which are absolute stellar abundance elemental ratios.

   The catalog enables the user to differentiate between thin disc and thick disc stars, a distinction made by \citet{Hinkel_2014, Hinkel_2016} using the kinematics method from \citet{Bensby_2003}. We use the catalog's distinction to construct the samples of thin and thick disc stars. However, the thick disc sample appears to contain possible halo stars. To separate thick disc and halo stars, we employ the same procedure from \citet{Bensby_2003} and the criteria outlined in \citet{Bensby_2014}.

   The separation of the thick disc and halo stars is performed using a kinematically derived probabilistic measure of which galactic substructure a star belongs to. This separation method uses the stars' galactic space velocity components (the radial velocity $U$, the rotational velocity $V$, and the vertical velocity $W$) compared to the characteristic substructures' galactic velocity dispersions as well as the observed populations' fraction of stars in the solar neighborhood \citep{Bensby_2003,Bensby_2014}.

   \citet{Bensby_2003} assume the galactic space velocities of the different stellar populations to have Gaussian distributions,
   \begin{equation}
       f(U, V, W)=k \cdot \exp \left(-\frac{U^{2}}{2 \sigma_{U}^{2}}-\frac{\left(V-V_{\mathrm{asym}}\right)^{2}}{2 \sigma_{V}^{2}}-\frac{W^{2}}{2 \sigma_{W}^{2}}\right),
        \label{eq:f(U,V,W)}
   \end{equation}
   where
   \begin{equation}
       k=\frac{1}{(2 \pi)^{3 / 2} \sigma_{U} \sigma_{V} \sigma_{W}}
   \end{equation}
   is the normalization constant \citep{Bensby_2003}. $\sigma_{U}$, $\sigma_{V}$, and $\sigma_{W}$ are the characteristic velocity dispersions, and $V$\textsubscript{asym} is the asymmetric drift, the values of these variables can be found in \citet{Bensby_2003}. We then use \citet{Bensby_2003} method to calculate the thick-disc-to-halo (TD/H) relative probability,
   \begin{equation}
       \mathrm{TD/H} = \frac{X_{\mathrm{TD}}}{X_{\mathrm{H}}} \cdot \frac{f_{\mathrm{TD}}}{f_\mathrm{H}},
        \label{eq:TD/H}
   \end{equation}
   where $X$ is the observed fraction of stars in the Solar neighbourhood from \citet{Bensby_2003} and $f$ is calculated from the star's velocities, see Eq. (\ref{eq:f(U,V,W)}). When the value of TD/H $<1$ then it is more likely a halo star \citep{Bensby_2014} and when TD/H $>1$ it is more likely a thick disc star. We determine the final overall stellar sample to be consisting of 1\,949 thin disc stars, 295 thick disc stars, and 41 halo stars. In Fig.~\ref{fig:toomre} we plot the overall stellar sample in a Toomre diagram illustrating the general differentiation of the stellar populations according to their total space velocities. However, the Toomre diagram highlights how the stellar populations total space velocities overlap due to the additional probabilistic separation criteria employed to categorize the stars.
   \begin{figure}[!ht]
   \centering
   \includegraphics[width=\hsize]{plot_toomrevelocity_stars.pdf}
      \caption{Toomre diagram demonstrating the stellar sample differentiated into the stellar populations. The red dots are likely thin disc stars, blue dots are likely thick disc stars, and orange dots are likely halo stars.} \label{fig:toomre}
   \end{figure}
\section{Methods}
\label{sec:methods}
   We used two theoretical models to numerically compute an expected solid planet structure. Using different models allows us to quantify model uncertainties to check for potential biases coming from the models.
\subsection{Model A}
\label{sec:modela}
   Model A is based on the comprehensive planet evolution code (\verb$completo21$) \citep{Mordasini_2012}. We compute an expected planetary \textit{M - R} relationship, methodology from \citet{Mordasini_2012}. The planetary structure is calculated by solving the 1D internal structure equations \citep{Bodenheimer_1986},
   \begin{eqnarray}
      \partial M/\partial R & = & 4 \pi R^2 \rho    \label{eq:intstr-mass} \\
      \partial P/\partial R & = & -G M \rho / R^2, \label{eq:intstr-pres}
   \end{eqnarray}
   using a modified polytropic equation of state (EoS) from \citet{Seager_2007}, as following,
   \begin{equation}
     \rho(P) = \rho_0 + cP^n.
   \end{equation}
   The main materials within the model treatment include pure iron, silicates (perovskite, enstatite) and ice, whose parameters $\rho_0$, $c$ and $n$ are from \citet{Seager_2007}. The EoS used provides the density as a function of pressure for a wide pressure range, however, it neglects the impact of temperature on density, and on the radius. Although the impact on the radius is small \citep{Valencia_2007, Grasset_2009}, a first-order temperature correction is used on the mean density for the current model version (see Appendix A from \citet{Linder_2019}).
\subsection{Model B}
\label{sec:modelb}
   Model B is based on the forward model for planetary structure inference by \citet{Dorn_2015}. Modifications to \citet{Dorn_2015} include different equations of state and the calculation of the adiabatic gradient in each layer directly from the equation of state (Haldemann et al. (in prep)).

   The radius of a planet is computed solving the static 1D internal structure Eqs.~\ref{eq:intstr-mass} and~\ref{eq:intstr-pres} given a planet mass $M_{total}$, a bulk composition ($\mathrm{N}_{\mathrm{Fe}}$/$\mathrm{N}_{\mathrm{Si}}$, $\mathrm{N}_{\mathrm{Mg}}$/$\mathrm{N}_{\mathrm{Si}}$, and water mass fraction $f_{water}$), surface temperature, and pressure. The planet is split into three layers, an iron core of mass $M_{core}$, a silicate mantle of mass $M_{rock}$, and a layer of water on the surface of mass $M_{water}$ (this latter condition is only included in the scenario where we consider a water layer). We use the EoS of \citet{Sotin_2007} for the mantle and the water layer; in the core for pressures above 240~GPa we use the EoS of \citet{Hakim_2018} and for lower pressures, we use the EoS of \citet{Fei_2016}. The temperature profile in each layer is assumed to be adiabatic as,
   \begin{equation}
     \frac{\partial T}{\partial R} = \frac{\partial P}{\partial R} \frac{P}{T} \left(\frac{\mathrm{d} \ln T}{\mathrm{d} \ln P}\right)_S.
   \end{equation}
   The choice of silicate phases in the mantle description of \citet{Sotin_2007} restricts the possible mantle compositions to values which fulfill the relation
   \begin{equation}
     1 \leq \left(\mathrm{N}_{\mathrm{Fe}}/\mathrm{N}_{\mathrm{Si}}\right)_{rock} + \left(\mathrm{N}_{\mathrm{Mg}}/\mathrm{N}_{\mathrm{Si}}\right)_{rock} \leq 2,
   \end{equation}
   where $\left(\mathrm{N}_{\mathrm{Fe}}/\mathrm{N}_{\mathrm{Si}}\right)_{rock}$ and $\left(\mathrm{N}_{\mathrm{Mg}}/\mathrm{N}_{\mathrm{Si}}\right)_{rock}$ are the elemental ratios in the rocky mantle. Assuming $\left(\mathrm{N}_{\mathrm{Fe}}/\mathrm{N}_{\mathrm{Si}}\right)_{rock} = 0$ as in model A would cause a large fraction of cases to be outside of the parametrization laid out by \citet{Sotin_2007}. Therefore, we adopt a value of $\left(\mathrm{N}_{\mathrm{Fe}}/\mathrm{N}_{\mathrm{Si}}\right)_{rock} = 0.3$ to minimize the number of cases outside the parametrization of \citet{Sotin_2007}. The assumed value of 0.3 for the iron to silicate mantle ratio is in between the expected Earth and Mars ratios \citep{Dreibus_1987}.
\subsection{Model inputs}
\label{sec:inputs}
   We employed part of the stoichiometric model from \citet{Santos_2017} where the stellar abundances of Fe, Si, Mg, C, O, He, and H are used to derive the important rock types and chemical species for the composition of terrestrial-like planets \citep{Seager_2007, Sotin_2007}. We use the iron mass fraction and water mass fraction as inputs for model A, thus, the pertinent rock types and species from the \citet{Santos_2017} model are MgSiO\textsubscript{3}, Mg\textsubscript{2}SiO\textsubscript{4}, SiO\textsubscript{2}, Fe, and H\textsubscript{2}O. We disregard the species used by \citet{Santos_2017} to calculate the summed mass percent of all heavy elements.

   We calculate, according to \citet{Santos_2017} stoichiometric model, the iron mass fraction $\left(f_{\mathrm{iron}}\right)$ of the rocky silicate-iron core and water mass fraction $\left(f_{\mathrm{water}}\right)$ present in the planetary bulk composition. We thus use the following relations from \citet{Santos_2017},
   \begin{eqnarray}
        f_{\text{iron}}=m_{\mathrm{Fe}} /\left(m_{\mathrm{Fe}}+m_{\mathrm{MgSiO} 3}+m_{\mathrm{Mg} 2 \mathrm{SiO} 4}+m_{\mathrm{SiO} 2}\right) \\
        f_{\text{water}}=m_{\mathrm{H} 2 \mathrm{O}} /\left(m_{\mathrm{H} 2 \mathrm{O}}+m_{\mathrm{Fe}}+m_{\mathrm{Mg} \mathrm{SiO} 3}+m_{\mathrm{Mg} 2 \mathrm{SiO} 4}+m_{\mathrm{SiO} 2}\right), \label{eq:fwater}
   \end{eqnarray}
   where $m_X=N_X\cdot \mu_X$, $N_X$ represents the number of atoms of each species $X$, and $\mu_X$ is their mean molecular weights; all $N_X$ values are computed relative to hydrogen \citep{Santos_2017}. We use part of the stoichiometric relations outlined in Appendix B of \citet{Santos_2017} to find the $N$ values of the different chemical species used in the mass fractions. As inputs to the models for mass fractions, we use \citet{Santos_2017} stoichiometric relations,
   \newline\noindent when $N_{\mathrm{Mg}} > N_{\mathrm{Si}}$,
   \begin{eqnarray}
        N_{\mathrm{O}}&=&N_{\mathrm{H}_{2} \mathrm{O}} + 3 N_{\mathrm{MgSiO}_{3}} + 4 N_{\mathrm{Mg}_{2} \mathrm{SiO}_{4}} \\
        N_{\mathrm{Mg}}&=&N_{\mathrm{MgSiO}_{3}} + 2 N_{\mathrm{Mg}_{2} \mathrm{SiO}_{4}} \\ N_{\mathrm{Si}}&=&N_{\mathrm{MgSiO}_{3}} + N_{\mathrm{Mg}_{2} \mathrm{SiO}_{4}},
    \end{eqnarray}
   alternatively, if $N_{\mathrm{Mg}} \leq N_{\mathrm{Si}}$,
   \begin{eqnarray}
        N_{\mathrm{O}}&=&N_{\mathrm{H}_{2} \mathrm{O}} + 3 N_{\mathrm{MgSiO}_{3}} + 2 N_{\mathrm{SiO}_{2}} \\
        N_{\mathrm{Mg}}&=&N_{\mathrm{MgSiO}_{3}} \\ N_{\mathrm{Si}}&=&N_{\mathrm{MgSiO}_{3}} + N_{\mathrm{SiO}_{2}}.
    \end{eqnarray}
   From these, the iron mass fraction and water mass fraction are calculated for every star in the stellar sample.

\begin{table}[!ht]
      \caption{Average values and standard deviations of iron mass fraction ($f_{\mathrm{iron}}$) and water mass fraction ($f_{\mathrm{water}}$) percentages expected in solid planets of different galactic populations. The $f_{\mathrm{water}}$ is considered only for solid planets forming past the ice line, prior to such, we consider 0\% $f_{\mathrm{water}}$ for all three stellar populations.}
      \label{table:iron_water_mass}
      \centering
      \begin{threeparttable}
         \begin{tabular}{l c c}
            \hline
            \noalign{\smallskip}
            &  $f_{\mathrm{iron}}$  (\%) & $f_{\mathrm{water}}$  (\%) \\
            \hline
            \noalign{\smallskip}
            This work &  & \\
            \noalign{\smallskip}
            Thin disc & $30.72 \pm3.76$ & $59.71 \pm9.52$\\
            Thick disc & $23.93 \pm4.68$ & $69.85 \pm8.63$\\
            Halo & $21.24 \pm4.03$ & $75.58 \pm6.20$\\
            \hline
            \noalign{\smallskip}
            \citet{Santos_2017} &  & \\
            \noalign{\smallskip}
            Thin disc & $31.974 \pm1.750$ & $59.713 \pm7.106$\\
            Thick disc & $24.305 \pm1.623$ & $72.179 \pm5.961$\\
            Halo & $23.110 \pm2.884$ & $83.990 \pm4.115$\\
            \hline
            \noalign{\smallskip}
            \citet{Cabral_2019}\tnote{1} &  & \\
            \noalign{\smallskip}
            Thin disc & $30.0 \pm0.8$ & $58.1 \pm0.4$\\
            Thick disc & $23.5 \pm1.7$ & $61.6 \pm0.9$\\
            Halo & $20.4 \pm0.2$ & $63.0 \pm0.1$\\
            \hline
            \noalign{\smallskip}
            Sun \citep{Lodders_2003} &  31.62 & 57.47 \\
            \hline
            \noalign{\smallskip}
         \end{tabular}
         \begin{tablenotes}
            \item[1] For comparison we select the Milky Way simulation from \citet{Cabral_2019}.
         \end{tablenotes}
      \end{threeparttable}
   \end{table}

   We compare the resulting water and iron mass fractions from the stellar populations in our sample with \citet{Santos_2017} and \citet{Cabral_2019} (see Table~\ref{table:iron_water_mass}, distribution plots for our stellar sample can be found in Appendix~\ref{appendix:a}, Figs.~\ref{ironfrac} and~\ref{waterfrac}). We note that there is good agreement between the different overall stellar samples regardless as to the whether the stellar populations were distinguished by chemical \citep{Santos_2017, Cabral_2019} or kinematic constraints (this work). The iron mass and water mass fraction trends are conserved across each set of results. We report that the thin disc population has the highest iron mass fraction and the halo population has the lowest iron mass fraction; whilst the water mass fraction is the lowest in the thin disc and the highest in the halo stars.

   For model B, we use absolute stellar abundance elemental ratios of Fe/Si and Mg/Si as well as the water mass fraction, the latter is calculated as outlined by the procedure above using \citet{Santos_2017} methodology. Within the elemental ratios, the Fe/Si variability is relatively distinct between the stellar populations; the highest ratio is in the thin disc and the lowest in the halo. This is expected and correlates with the iron mass fraction trends seen in Table~\ref{table:iron_water_mass}. A more detailed discussion pertaining to input distributions and distinctiveness between stellar populations is found in Appendix~\ref{appendix:a}. The Mg/Si ratio is not significantly variable between stellar populations and does not allow us to distinguish between stellar populations (see Figs.~\ref{fesi} and~\ref{mgsi}).

   Both models necessitate surface temperature inputs; for our purposes we calculate a planetary equilibrium temperature as a function of distance from the host star (Eq.~\ref{temp}), repeating this procedure for every star. We use the following relation to calculate the planetary equilibrium temperature, $T_{eq}$ as,
   \begin{equation}
       T_{eq}=\left(\frac{L_{\star}\left(1-A_{b}\right)}{16 \sigma \pi a^{2}}\right)^{1 / 4},
       \label{temp}
   \end{equation}
   where $L_\star$ is the stellar luminosity, $A_{b}$ is the planet albedo which we fix at 0.3, $\sigma$ is the Stefan-Boltzmann constant, and $a$ is the planet's orbital distance from its host star. We select planetary orbital distances of 0.1 and 4 AU.
   For each star, an iron-mass fraction, water-mass fraction, Fe/Si, Mg/Si, and an expected planetary equilibrium temperature are calculated as inputs to the models. The variability in stellar abundances within one stellar population is expected to provide a distribution of radii per fixed mass in the range of 1 to 10 $M_{\oplus}$. We run both models at each fixed mass for the whole samples of each stellar population; this allows for a median of the planetary radii distribution to be calculated per stellar population per fixed mass to find the typical \textit{M - R} relationship.

\subsection{Validation}
\label{sec:validation}
   We tested these models against a composition predicted by the solar photosphere chemical abundances from \citet{Lodders_2003} and a 0.01\% water mass fraction to account for current estimates of water on Earth. Running the models for a 1 $M_{\oplus}$ planet at an orbital distance of 1 AU from the Sun, model A yields a planet of 0.961 $R_{\oplus}$ and model B yields a planet of 1.013 $R_{\oplus}$. The two models differ systematically in the radius given the solar derived Earth composition. As such, we do not delve into a comparison of individual radii values between the models but, rather, how the different stellar populations' chemical composition impact the expected relative planetary radii differences between the stellar populations.
\section{Results}
\label{sec:results}
   \begin{figure*}
      \includegraphics[width=\textwidth]{plot_together_01AU.pdf}
      \cprotect\caption{At 0.1 AU, the expected \textit{M - R} relationships of silicate-iron planets in different galactic stellar populations are in the left panels and violin plots demonstrating expected radii distributions at 3 $M_{\oplus}$ are in the right panels. The two top panels are results obtained using model A and the two bottom panels are from model B.}
         \label{fig:01au}
   \end{figure*}
   \begin{figure*}
      \includegraphics[width=\textwidth]{plot_together_4AU.pdf}
      \cprotect\caption{Same plots as in Fig.~\ref{fig:01au} calculated at 4 AU for silicate-iron-water planets. The water mass fraction is now included to form a water ice layer around the rocky silicate-iron core. The violin plots in the right panels illustrate the expected radii distributions for 10 $M_{\oplus}$ planets.}
         \label{fig:4au}
   \end{figure*}
   We present the planetary \textit{M - R} relationships for synthetic solid planets for a mass range of 1 to 10 $M_{\oplus}$ computed at 0.1 AU for a silicate-iron composition (Fig.~\ref{fig:01au}) and at 4 AU for a silicate-iron-water composition (Fig.~\ref{fig:4au}) by both models A and B. In the left panels of Figs.~\ref{fig:01au} and~\ref{fig:4au}, each stellar population yields different median solid planet \textit{M - R} relationships; the dashed red lines are the expected typical solid planets orbiting around thin disc stars; the solid blue lines are planets around thick disc stars; the dotted orange lines are planets around halo stars. In the right panels of Figs.~\ref{fig:01au} and~\ref{fig:4au}, violin plots at 3 $M_{\oplus}$ and 10 $M_{\oplus}$, respectively, illustrate the radii distributions of the solid planets for the thin and thick disc stellar populations. At 3 $M_{\oplus}$ at 0.1 AU and 10 $M_{\oplus}$ at 4 AU, for the three stellar population inputs, both models produce expected median planetary radii that are currently abundant in solid exoplanet observations \citep{Fulton_2017}.

   The two cases, silicate-iron planets at 0.1 AU and silicate-iron-water planets at 4 AU from their host star, were chosen to demonstrate two types of solid planets. The first case investigates a rocky silicate-iron core with no atmosphere and the ulterior case investigates a rocky silicate-iron core with a significant water ice layer. At 0.1 AU the expected planetary equilibrium temperature is on average between 850 to 880~K depending on the stellar population. Due to these high temperatures, no volatiles are taken into considerations for the scenario at 0.1 AU since they would have likely been evaporated. Instead, we focus on the effect of the stellar populations' iron content, placed in the rocky silicate-iron core, on the \textit{M - R} relationships. Seen in Fig.~\ref{fig:01au}, for both models, the thin disc stars with the highest iron-mass fraction and Fe/Si ratio yield the smallest typical planetary radii. The halo stars with the lowest iron-mass fractions and Fe/Si ratios yield planets with the largest typical radii at fixed masses on the \textit{M - R} diagram.

   Since we use the whole sample of stars per stellar populations as inputs to the models, we have a distribution of planetary radii per fixed mass and per stellar population. There is variability in the stellar photospheric abundances within each sample stellar population (see Figs.~\ref{ironfrac},~\ref{waterfrac},~\ref{fesi}, and~\ref{mgsi}), thus there is a distribution of expected planetary radii as illustrated by the violin plots in the right panels of Figs.~\ref{fig:01au} and~\ref{fig:4au}.

   At 0.1 AU the expected planetary radii distributions for a 3 $M_{\oplus}$ solid planet are displayed in the violin plots in Fig.~\ref{fig:01au}. This mass was chosen since it produces median radii between 1.334 to 1.413 $R_{\oplus}$ thereby being part of the abundant observations of transiting  super-Earths \citep{Fulton_2017}. Noticeably, the planetary radii distributions between the thin and thick disc stellar populations overlap. However, the median radius, as indicated by the thick dashed lines, demonstrate the difference in the expected typical radius of the different stellar populations. At 3 $M_{\oplus}$, we report a 1.48\% difference between the thick disc (1.354 $R_{\oplus}$) and thin disc (1.334 $R_{\oplus}$) median planetary radii and a 0.52\% between the halo (1.361 $R_{\oplus}$) and thick disc planetary radii using model A. As for model B, we calculate a 2.04\% radius difference between the thick disc (1.404 $R_{\oplus}$) and thin disc (1.377 $R_{\oplus}$), and a 0.66\% difference between the halo (1.413 $R_{\oplus}$) and thick disc. For planets at 0.1 AU, we find that there is an expected 1.51\% mean planetary radius difference between the thick and thin disc across the 1 to 10 $M_{\oplus}$ planets from model A and a 2.04\% radius difference from model B. Between the halo and thick disc, the mean planetary radius difference for 1 to 10 $M_{\oplus}$ is 0.53\% from model A and 0.69\% from model B.

   In Fig.~\ref{fig:4au} we present the \textit{M - R} relationships for solid planets with a water ice layer, namely silicate-iron-water planets. The median planetary equilibrium temperatures computed for the stellar samples vary between 135 and 140~K, thereby condensing the refractory species as well as volatiles. We concern ourselves only with H\textsubscript{2}O \citep{Dorn_2017_2}. We also consider that all the H\textsubscript{2}O present in the protoplanetary disc is condensed during the accretion process. In the inputs, the water mass fraction is largest in the halo stars and lowest in the thin disc stars; this results in compounding the iron mass fraction variability to form denser planets around thin disc stars compared to thick disc and halo stars. From the \textit{M - R} relationships in Fig.~\ref{fig:4au}, the smallest planets orbit around thin disc stars and the largest planets around halo stars at fixed mass. At 10 $M_{\oplus}$ the models compute planets of radii comparable to \citet{Fulton_2017} abundant sub-Neptunes and \citet{Grasset_2009} 10 $M_{\oplus}$ solid planets with 60 to 80\% water mass fractions. In the violin plots of Fig.~\ref{fig:4au} we show the distributions of planetary radii. The median radius difference between the thick disc (2.461 $R_{\oplus}$) and thin disc (2.384 $R_{\oplus}$) is of 3.24\%, whilst between the halo (2.497 $R_{\oplus}$) and thick disc, it is a difference of 1.46\% using model A. With model B, we get similar results, there is a 2.88\% difference in median planetary radii between the thick disc (2.353 $R_{\oplus}$) and the thin disc (2.297 $R_{\oplus}$), and a 1.20\% difference between the halo (2.381 $R_{\oplus}$) and the thick disc. Overall for the selected 1 to 10 $M_{\oplus}$ range at 4 AU, we find that there is an expected 3.26\% radius difference between the thick and thin disc from model A and a 2.93\% radius difference from model B. Between the halo and thick disc, the mean planetary radius difference for 1 to 10 $M_{\oplus}$ is 1.49\% from model A and 1.24\% from model B.

   For model A and to a lesser extent in model B, at both orbital distances, there are secondary peaks in the thick disc planetary radii distributions within the violin plots per fixed mass. These double peaks in the thick disc radii distributions are to the secondary peaks present in the iron fraction and Fe/Si distributions. The input for model A, the thick disc iron mass fraction distribution, has a primary peak centered at 22.1\% and a secondary peak is identified at 29.9\% (see Fig.~\ref{ironfrac}). For model B, the thick disc Fe/Si input distribution also has a similar feature, the primary peak is at 0.57 and is accompanied by a secondary peak at 0.86 (see Fig.~\ref{fesi}). The secondary peaks in the thick disc input parameters aforementioned are caused by an overabundance of thick disc stars with high Fe/Si ratios, this is then propagated into the thick disc solid planet radii distributions as seen in the violin plots of Figs~\ref{fig:01au} and~\ref{fig:4au}. In Appendix~\ref{appendix:a}, a more detailed treatment is provided as to why these two thick disc parameter inputs demonstrate bimodal Gaussian distributions.

   From these results, we see a consistent statistical difference between the solid planets that would form around the different stellar populations; at a fixed mass the typical planets around thin disc stars have smaller radii than planets around thick disc stars and halo stars for both cases at 0.1 and 4 AU (for a complete median radius and radius difference per stellar population and model at 0.1 AU, see Table~\ref{table:rad01AU}, and at 4 AU, see Table~\ref{table:rad4AU}).
   \begin{figure}[!h]
      \includegraphics[width=\hsize]{violin_together_modela.pdf}
      \cprotect\caption{Mass aggregate comparison of the planetary radii distribution percentage difference from the solar-derived expected planets at 0.1 and 4 AU as computed by model A.}\label{fig:modela}
   \end{figure}
   \begin{figure}[!h]
      \includegraphics[width=\hsize]{violin_together_modelb.pdf}
      \cprotect\caption{Same plots as in Fig.~\ref{fig:modela} using model B.}\label{fig:modelb}
   \end{figure}

   To compare the three stellar population radii distributions, we benchmark these against the expected model-calculated solar system planets at 0.1 and 4 AU using the methodology outlined in Sec.~\ref{sec:inputs} applied to \citet{Lodders_2003} solar photospheric abundances. The range of planet masses within the stellar populations are aggregated and compared to the expected solar planets at 0.1 and 4 AU from 1 to 10 $M_{\oplus}$. The violin plots in Figs.~\ref{fig:modela} and~\ref{fig:modelb} illustrate the planetary radius difference per stellar population compared to the solar-derived planet benchmark for models A and B, respectfully. From these, the planetary radii percentage difference distributions are seen to differ between the stellar populations across the aggregate mass range. The planets orbiting around halo stars have a larger percentage difference from the solar benchmark than those around thin disc or thick disc stars. Across stellar populations and models, the presence of the water ice layer in the silicate-iron-water planets at 4 AU increases the planetary radii compared to the dry silicate-iron planets at 0.1 AU.

\section{Discussion and Outlook}
\label{sec:discussion}
   The results obtained from the kinematically separated galactic stellar populations demonstrate that there is a physical difference in the solid planet \textit{M - R} relationships between the thin disc, thick disc, and halo populations. For both models at 0.1 AU, the relative radii differences between the stellar populations increase with mass. However, at 4 AU, for both models, an increase in mass results in a decrease in the relative radii differences between the stellar populations. This is due to the larger compressibility of the water ice layer at higher pressures and thus reducing the difference in radii at larger planetary masses with larger water masses.

   The stellar abundance variability between the galactic stellar populations was demonstrated by \citet{Santos_2017}, \citet{Cabral_2019}, and confirmed again with our different stellar sample and separation method. We show that the chemical distinctiveness of the various galactic stellar populations can be partially retrieved after probabilistic kinematic differentiation for our initial stellar sample from the Hypatia catalog, see Appendix~\ref{appendix:a}.

   The bulk composition of solid exoplanets impacts the \textit{M - R} relations as demonstrated by \citet{Mordasini_2012} and \citet{Dorn_2017_2} when testing internal structure models. Thereby from the stellar-derived bulk planetary compositions, we confirm these results as being coherent with the literature \citep{Mordasini_2012, Dorn_2017_2, Santos_2017, Cabral_2019}. The planetary \textit{M - R} relationship differences between the stellar populations are demonstrated using two different models and are persistent for both dry silicate-iron planets at 0.1 AU and water-rich silicate-iron-water planets at 4 AU. The two models, as seen in Sec.~\ref{sec:validation}, for a solar-derived Earth composition, do not compute an exact 1 $R_ {\oplus}$ but rather show a systematic difference. As such, the exact planetary radii calculated for the three stellar populations is not the core focus of this work, rather we aim to show the systematic median planetary radii differences between the stellar populations with two separate models.

   This work rests on the basic assumption that the stellar photosphere's chemical composition approximates solid planets' bulk compositions well. Many studies have provided evidence that this approximation is reasonable when examining the refractory species' abundances in chondritic meteorites, Earth, Venus, and Mars compared to our Sun's photosphere \citep{Morgan_1980, McDonough_1995, Drake_2002, Lodders_2003, Khan_2008}. This presumption, that stellar abundance is a good proxy for solid planet composition, is currently widely used in planetary bulk composition calculations and planet internal structure models \citep{Santos_2015, Thiabaud_2015, Brugger_2017, Santos_2017, Cabral_2019, Wang_2019}. The iron mass fraction relationship between the host star and planet was further examined by \citet{Santos_2015} where they show that stars hosting exoplanets, CoRoT-7, Kepler-10, and Kepler-93, have photospheric abundances that are thought to be reflected in their transiting rocky planets. In contrast, Mercury and dense planets, i.e. K2-106 b or K2-229 b, who are often very close in to their host stars, have demonstrated larger iron cores amounting up to possibly 80\% of the planetary mass, thereby differing considerably from their host star's composition \citep{Guenther_2017, Santerne_2018}.

   The present-day solid planet composition may vary from the expected initial bulk composition due to processes happening in the protoplanetary disc during formation and after disc dispersal. Deviations in the planetary bulk composition could be the result of fractionation and incomplete condensation \citep{Wasson_1974}, partial volatile evaporation \citep{Braukmuller_2018, Lichtenberg_2019}, varying accretion and differentiation mechanisms \citep{Fischer_2017}, initial location, potential migration in the protoplanetary disc and migration post disc \citep{Mordasini_2012, CarterBond_2012}, and collisional stripping post disc formation \citep{Marcus_2010}. These possible evolutionary complications during planet formation are hindrances to using the stellar photospheric abundances as proxies for solid planet compositions. These impacts are believed to be less significant for the refractory species inclusion, as a scenario at 0.1 AU, but are tantamount to the water mass fraction as in our scenario at 4 AU.

   At 4 AU, in both models, we use a pure water ice layer as a substitute for all other ice types (commonly these include CO, CO\textsubscript{2}, CH\textsubscript{4}, NH\textsubscript{3}). According to \citet{Dorn_2017_2}, this substitution is reasonable since O is more abundant than C and N, and water condenses at higher temperatures than other ice species. Another minor limitation comes from our decision to not compute any atmosphere neither at 0.1 nor at 4 AU. Furthermore, we do not take into account the stellar abundance uncertainties, their impact is negligible on the final solid planet radii.

   In regards to the planetary equilibrium temperatures calculated at 0.1 and 4 AU, we do not use bolometric corrections between the different star types and rather just make a general statement of the expected temperature at these orbital distances. The temperature is a rough estimate used to control the type of chemical species that condense into the bulk planetary composition at the selected orbital distances. We use this to determine whether to include the water mass fraction as calculated by Eq.~(\ref{eq:fwater}) or to assume negligible amounts of water are present and use a water mass fraction of 0.

   The results show a solid planet \textit{M - R} difference across the two computational models for a range of fixed masses from 1 to 10 $M_{\oplus}$ at two separate orbital distances of 0.1 and 4 AU from their host star. The key idea can be seen in the statistics of the distributions, it cannot be used as a stand alone result with individual exoplanet characterizations within these stellar populations.

   To confirm these results, observations of a statistically important number of solid exoplanets are required, particularly for thick disc and halo stars. Presently, observers have mainly characterized planets around thin disc stars. According to \citet{Adibekyan_2012_2}, for given sub-solar metallicities, the planet occurrence rate is thought to be greater in the thick disc compared to the thin disc. Additionally, \citet{Bashi_2019} provide evidence that the small-planet occurrence rate is high for both iron-poor high velocity stars such as thick disc ones, and iron-rich low velocity stars, thin disc stars. There have already been observations of some thick disc stars hosting exoplanets, namely Kepler-10 and Kepler-444 \citep{Dumusque_2014, Campante_2015} as well as a halo star, Kapteyn \citep{Anglada-Escude_2014} and more observations can be expected in the future. As new space-based telescopes (including PLATO) precisely characterize solid planets in various stellar populations, planetary \textit{M - R} relations will be better constrained. From precise planetary \textit{M - R} measurements we will also be able to confirm or disbar the idea of using the stellar photosphere composition as a proxy for solid planets' bulk compositions. Lastly, our result may help to assess the potential habitability of planets around stars in different stellar populations.
\begin{acknowledgements}
      A.M. acknowledges the opportunity to conduct research at the Physikalisches Institut, Universität Bern. J.H. acknowledges the support from the Swiss National Science Foundation under grant 200020$\_$172746. C.M. acknowledges the support from the Swiss National Science Foundation under grant BSSGI0$\_$155816 ``PlanetsInTime''. Parts of this work have been carried out within the frame of the National Center for Competence in Research PlanetS supported by the SNSF. We thank the referee for their constructive comments and suggestions.
\end{acknowledgements}
\bibliographystyle{aa}

redpajama_set_name: RedPajamaArXiv


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text:
On chromium direct ETAAS determination in serum and urine
Nadica Todorovska ,	Irina Karadjova ,	Sonja Arpadjan ,	Trajce Stafilov
A simple, accurate and reliable method for direct electrothermal atomic absorption spectrometric (ETAAS) determination of chromium in serum and urine samples without any preliminary sample pretreatment is described. Instrumental parameters are optimized in order to define: the most suitable atomizer, optimal temperature program and efficient modifier. An appropriate quantification method is proposed taking into account a matrix interference study. Pyrocoated graphite tubes and wall atomization, pretreatment temperature of 700 °C, atomization temperature of 2600 °C, hydrogen peroxide as modifier and standard addition calibration are recommended. The accuracy of the method proposed for Cr determination in serum and urine was confirmed by comparative analysis of parallel samples after wet or dry ashing as well as by the analysis of two certified reference materials: Serum, Clin Rep 1 and Lypochek Urine, level 1. The detection and determination limits achieved for both matrices are 0.08 µg/L and 0.15 µg/L respectively. The relative standard deviation varied between 15 and 18 % for the chromium content in the samples in the range 0.08–0.2 µg/L and between 4 and 7 % for the chromium content in the range 0.2–2.0 µg/L for both matrices. [...]
Chromium electrothermal atomic absorption spectrometry serum urine
1 - 3 - 2007
Nadica Todorovska
Institute of Preventive Medical Care and Toxicology, Military Health Institution Center, 1000, Skopje, Republic of Macedonia
Irina Karadjova
Faculty of Chemistry, University of Sofia, 1126, Sofia, Bulgaria
Sonja Arpadjan
Trajce Stafilov
Institute of Chemistry, Faculty of Science, St. Cyril and Methodius University, 1001, Skopje, Republic of Macedonia, trajcest@iunona.pmf.ukim.edu.mk
[1] V. Ducros: "Chromium metabolism. A literature review", Biol. Trace Elem. Res., Vol. 32, (1992), pp. 65–77. http://dx.doi.org/10.1007/BF02784589[Crossref]
[2] D.G.J. Barceloux: "Chromium", Clin. Toxicol., Vol. 37, (1999), pp. 173–194. http://dx.doi.org/10.1081/CLT-100102418[Crossref]
[3] R. Cornelis, F. Borguet, S. Dyg and B. Griepink: "Chromium speciation studies in human plasma and stability studies of Cr(III) and Cr(VI) species in a candidate water reference material", Mikrochim. Acta, Vol. 109, (1992), pp. 145–148. http://dx.doi.org/10.1007/BF01243228[Crossref]
[4] A. Taylor and P. Green: "Determination of chromium in serum by electrothermal atomisation atomic absorption spectrometry using totally pyrolytic graphite cuvettes, J. Anal. At. Spectrom., Vol. 3, (1988), pp. 115–118. http://dx.doi.org/10.1039/ja9880300115[Crossref]
[5] B. Baumgardt, E. Jackwerth, H. Otto and G. Tőlg: "Trace analysis to determine heavy metal load in lung tissue. A contribution to substantiation of occupational hazards", Int. Arch. Occup. Environ. Health, Vol. 58, (1986), pp. 27–34. http://dx.doi.org/10.1007/BF00378537[Crossref]
[6] International Program on Chemical Safety: Environmental Health Criteria-Chromium, Vol. 61, WHO, Geneva, 1988.
[7] IARC Monographs on the evaluation of the carcinogenic risk of chemicals to humans, Vol. 7, (1987), p. 165.
[8] C. Minoia and A. Cavalleri: "Chromium in urine, serum and red blood cells in the biological monitoring of workers exposed to different chromium valency states", Sci. Total Environ., Vol. 71, (1988), pp. 323–327. http://dx.doi.org/10.1016/0048-9697(88)90204-5[Crossref]
[9] S. Farhurst and C.A. Minty: The Toxicity of Chromium and Inorganic Chromium Compounds, Toxicity Review 21, The Health and Safety Executive of the U.K., London, 1989, p. 243.
[10] Health effects assessment for hexavalent chromium, Environmental Protection Agency, 49, VI:EPA/540/1-86/019, 1984.
[11] J.N. Maks, M.A. White and A.R. Boran: "Direct determination of chromium in urine by graphite-furnace atomic-absorption spectrophotometry", At. Spectrosc., Vol. 9, (1988), pp. 73–75.
[12] X.Q. Shan, Y. Zheng and Z.M. Ni: "Determination of chromium in urine by graphite-furnace atomic-absorption spectrometry after solvent-extraction with high-molecular-weight secondary alkyl amine", At. Spectrosc., Vol. 11, (1990), pp. 116–124.
[13] D.C. Paschal and G.G. Bailey: "Determination of chromium in urine with graphite-furnace atomic-absorption spectroscopy using Zeeman correction", At. Spectrosc., Vol. 12, (1991), pp. 151–154.
[14] R. Rubio, A. Sahuquillo, G. Rauret, L.G. Beltran and P. Quevauviller: "Systematic study of chromium determination in urine by graphite-furnace atomic-absorption spectrometry", Anal. Chim. Acta, Vol. 283, (1993), pp. 207–212. http://dx.doi.org/10.1016/0003-2670(93)85224-8[Crossref]
[15] E.A.-C. Cimadevilla, K. Wrobel, J.M.M. Gayon and A. Sanz-Medel: "Determination of chromium in biological fluids by electrothermal atomic absorption spectrometry using wall, platform and probe atomization from different graphite surfaces", J. Anal. Atom. Spetrom., Vol. 9, (1994), pp. 117–123. http://dx.doi.org/10.1039/ja9940900117[Crossref]
[16] V.A. Granadillo, L. Parra de Machado and R.A. Romero: "Determination of total chromium in whole blood, blood components, bone, and urine by fast furnace program electrothermal atomization AAS and using neither analyte isoformation nor background correction", Anal Chem., Vol. 66, (1994), pp. 3624–3631. http://dx.doi.org/10.1021/ac00093a015[Crossref]
[17] P. Borella, A. Bargellini, S. Salvioli and A. Cossarizza: "Use of flameless atomic absorption spectroscopy in immune cytolysis for nonradioactive determination of killer cell activity", Clin Chem., Vol. 42, (1996), pp. 319–25.
[18] P. Apostoli, G. Maranelli, P. G. Duca, P. Bavazzano, A. Bortoli, A. Cruciatti, G. Elia, C. Minoia, R. Piccinini, E. Sabbioni, G. Sciarra and C. Soave: "Reference values of urinary chromium in Italy", Int. Arch. Occup. Environ. Health, Vol. 70, (1997), pp. 173–179. http://dx.doi.org/10.1007/s004200050203[Crossref]
[19] O. Mestek, T. Zima, M. Suchanek and J. Zilkova: "Direct determination of chromium in blood serum by electrothermal-atomization atomic absorption spectrometry", Chemicke Listy, Vol. 92, (1998), pp. 746–750.
[20] S.P. Quinaia and J.A. Nobrega: "A critical evaluation of the graphite furnace conditions for the direct determination of chromium in urine", Fresenius J. Anal. Chem., Vol. 364, (1999), pp. 333–337. http://dx.doi.org/10.1007/s002160051345[Crossref]
[21] J.L. Burguera, M. Burguera, C. Rondon, L. Rodriguez, P. Carrero, Y.P. de Pena and E. Burguera: "Determination of chromium in urine by electrothermal atomic absorption spectrometry using different chemical modifiers", J. Anal. At. Spectrom., Vol. 14, (1999), pp. 821–825. http://dx.doi.org/10.1039/a808239a[Crossref]
[22] Y.L. Huang, I.C. Chuang, C.H. Pan, C. Hsiech, T.S. Shi and T.H. Lin: "Determination of chromium in whole blood and urine by graphite furnace AAS", At. Spectrosc., Vol. 21, (2000), pp. 10–16.
[23] M. Soylak, I. Narin, L. Elci and M. Dogan: "Copper, nickel, lead and chromium determination by atomic absorption spectrometry in urine after enrichment/separation on Amberlite XAD-2000", Fresenius Environ. Bulletin, Vol. 11, (2002), pp. 132–136.
[24] B.R. Nunes, C.G. Magalhaes and J.B.B. da Silva: "Fast determination of chromium in human serum by electrothermal atomic absorption spectrometry", J. Anal. At. Spectrom., Vol. 17, (2002), pp. 1335–1338. http://dx.doi.org/10.1039/b202912j[Crossref]
[25] F. Barbosa, S.S. de Souza, D. Santos and F.J. Kung: "Evaluation of electrodeposited tungsten chemical modifier for direct determination of chromium in urine by ETAAS", Microchem. J., Vol. 78, (2004), pp. 7–13. http://dx.doi.org/10.1016/j.microc.2004.02.016[Crossref]
[26] J. Begerow, M. Turfeld and L. Dunemann: "New horizons in human biomonitoring of environmentally and occupationally relevant metals-sector-field ICP-MS versus electrothermal AAS", J. Anal. Atom. Spetrom., Vol. 15, (2000), pp. 347–352. http://dx.doi.org/10.1039/a909056h[Crossref]
[27] I. Rodushkin, F. Odman, R. Oloofsson and M.D. Axelsson: "Determination of 60 elements in whole blood by sector field inductively coupled plasma mass spectrometry", J. Anal. Atom. Spetrom., Vol. 15, (2000), pp. 937–944. http://dx.doi.org/10.1039/b003561k[Crossref]
bwmeta1.element.-psjd-doi-10_2478_s11532-006-0055-8


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Morgan County Herald: Obituaries
Morgan County Herald
Beverly Waterford
Columns & Commentary
Youth Schools
A meeting of the Board of Directors of the Muskingum Watershed Conservancy District will be held at 9 a.m. Friday, Jan. 22. Because of Executi…
posted: January 13
Health Department administering vaccines in Morgan County
The Ohio State University Extension - Morgan County
Health Department News
Finley Fire Equipment announces merger with Atlantic Emergency Solutions
Lady Raiders win two MVL Small Division battles
The Morgan Lady Raider basketball program registered important Muskingum Valley League Small School Division victories over Crooksville and C…
THE CADET REPORT: Lady Cadets' rally shoots down top-ranked Berne Union Rockets
Raiders escape Philo, fall to John Glenn
The Wildcat Watch: Lady Cats 'blow out' Tornadoes
CADET REPORT: Girls basketball team wins two
Johnny Keith Nichols
Posted: January 19, 2021 - 10:12 pm
Johnny Keith Nichols, 69, of Waterford, Ohio, died on Saturday, Jan. 16, 2021, at Ohio State University Hospital.
Rena M. Thompson
Posted: January 18, 2021 - 4:01 pm
Rena M. Thompson, 60, of Chesterhill, passed away on Friday, Jan. 15, 2021, at her residence.
Krista F. Jordan
Krista F. Jordan, 14, of Glouster, passed away Friday, Jan. 15, 2021.
Beverly J. Sealock
Beverly J. Sealock, 69, of McConnelsville, passed away at 12:10 a.m. Thursday, Jan. 14, 2021, at her home.
Kenneth Welcome Robertson
Louisburg, North Carolina
Tracy D. Van Horn
Tracy D. Van Horn, 55, of Crooksville, passed away at 9:26 p.m. Thursday, Jan. 14, 2021, at Genesis Hospital in Zanesville.
Margaret Arlene Mitchell
Margaret Arlene Mitchell, 89, of Cedar Park, Texas, formerly of Junction City, Ohio, passed away on Friday, Jan. 1, 2021, at Cedar Park Regional Medical Center after suffering a stroke.
Myra Elaine Mayle
Myra Elaine Mayle, 61, of McConnelsville, Ohio, passed away Tuesday, Jan. 12, 2021.
Starla F. Tabler
Starla F. Tabler, of Malta, formerly of Broadwell, passed away on Wednesday, Jan. 13, 2021.
Gail Franklin Brawley
Gail Franklin Brawley, 103, of Stewart, Ohio, passed away on Wednesday, Jan. 13, 2021, at Kimes Nursing Home in Athens, Ohio.
Carl Ronald 'Ronnie' Wolfe
Carl Ronald "Ronnie" Wolfe, 82, of Stockport, passed away peacefully on Thursday, Jan. 7, 2021, at Riverside Methodist Hospital in Columbus.
Reva E. McCune
Reva E. McCune, 68, of McConnelsville, passed away Friday, Jan. 8, 2021, at her home.
William E. Garrett
William E. Garrett, 85, of McConnelsville, passed away at 4:36 a.m. Friday, Jan. 8, 2021, at his home.
Pearl May Rollyson
Pearl May Rollyson, 84, of Malta, passed away Wednesday, Jan. 6, 2021, at the Highland Oaks Health Center.
Sherri Ann Coulson
Sherri Ann Coulson, 67, passed away at Genesis Hospital following a short illness. She was a resident at Adams Lane Care Center since 2015.
Pegge Elliott McHugh
Pegge Elliott McHugh, 82, of Standish, Maine, passed away on Tuesday, Dec. 29, 2020.
Ronald Ray Stowers
Ronald Ray Stowers, 75, of Stockport, passed away on Sunday, Jan. 3, 2021, at his residence.
Robert J. Downs
Robert J. Downs, 89, of Chesterhill, died in the morning hours of Wednesday, Dec. 30, 2020, while at home on his hilltop farm after just a two-week diagnosis of glioblastoma.
Barbara A. Kirkpatrick
Barbara A. Kirkpatrick, 87, of McConnelsville, passed away on Friday, Jan. 1, 2021, at the Genesis Hospital in Zanesville.
Billy Battle
Posted: December 31, 2020 - 12:11 pm
Billy Battle, 72, of Chesterhill, Ohio, passed away at home on Wednesday, Dec. 16, 2020.
Shirley M. Frye
Posted: December 31, 2020 - 11:22 am
Shirley M. Frye, 84, of Malta, died peacefully with family by her side on Tuesday, Dec. 29, 2020, at Altercare Care Center in Zanesville, Ohio.
Raymond Gifford Walker
Raymond Gifford Walker, 69, of Sardis, Ohio, passed away Saturday, Dec. 26, 2020, at Wheeling Hospital.
F. Michael Totman
Posted: December 28, 2020 - 1:29 pm
F. Michael Totman, 75, of Zanesville, formerly of McConnelsville, passed away on Thursday, Dec. 24, 2020, at the Genesis Hospital in Zanesville.
Keller Notice
Mary Catherine (Moler) Keller, 78, of Zanesville, formerly of McConnelsville, passed away at 12:25 a.m. Saturday, Dec. 26, 2020, at the Hospice wing of Genesis Hospital, in Zanesville, due to Progressive Supranuclear Palsy (PSP) complicated by the effects of COVID-19.
Wava G. McNabb
Wava G. McNabb, 89, formerly of Beverly, Ohio, passed on, Thursday, Dec. 24, 2020, at 5:15 a.m. after a short battle with cancer, at Worthington Nursing Home.
Merle P. 'Mike' Rucker
Merle P. "Mike" Rucker, 92, of Stockport, Ohio, died on Sunday, Dec. 20, 2020, at Genesis Hospital in Zanesville.
Gary Ray Cain
Gary Ray Cain, 67, of McConnelsville, passed away on Saturday, Dec. 19, 2020, at his residence.
Richard L. Strode, Sr.
Richard L. Strode, Sr., 84, of Malta, passed away Thursday, Dec. 17, 2020, at his home.
Elizabeth (Irene) Flowers
Elizabeth (Irene) Flowers, of Kilvert, Ohio, 93, died on Monday, Dec. 14, 2020, in The Laurels of Athens.
Beulah M. Campbell
Posted: December 15, 2020 - 8:45 am
Beulah M. Campbell, 92, of McConnelsville, passed away Sunday, Dec. 13, 2020, at her residence.
Juanita (Willard) Adkins
Juanita (Willard) Adkins, 96, passed away on Sunday, Dec. 13, 2020, at the Highland Oaks Nursing Home in McConnelsville.
Sharon L. Medburg
Sharon L. Medburg, 80, of Beverly, passed away on Sunday, Dec. 13, 2020, at the Riverside Landing Nursing Home in McConnelsville.
Ryan J. Horner
Ryan J. Horner, 37, of Stockport, passed away Friday, Dec. 4, 2020, at his home.
Emma Eloise Smith Ball
Emma Eloise Smith Ball, 96, of Glouster, Ohio, passed away at Lafayette Point, West Lafayette, Ohio, on the 13th of December, 2020.
Kathleen L. High
Kathleen L. High, 92, of Westerville, peacefully went to be with the Lord to join her husband, Charles "Charlie" High.
Brenda K. Clarke
Brenda K. Clarke, 56, of Malta, passed away on Saturday, Dec. 12, 2020, at her residence.
James E. Wilson, 82, of McConnelsville, passed away Sunday, Dec. 13, 2020, at the Highland Oaks Health Center in McConnelsville.
Barbara Ann Perrine
Barbara Ann Perrine, 60, of McConnelsville, passed away on Tuesday, Dec. 8, 2020, at the Highland Oaks Health Center in McConnelsville.
Robert E. Cunningham
Robert E. Cunningham, 78, of McConnelsville, passed away Friday, Dec. 11, 2020, at the Highland Oaks Health Center.
Bryan David Heigley
Bryan David Heigley, 42, of Deavertown, Ohio, died at 8:08 p.m. Thursday, Dec. 10, 2020, at his home with his parents and entire loving family surrounding him in love.
Dolores M. Webb
Dolores M. "Dottie" Webb, 85, of Stockport, passed away at 6:20 p.m. Tuesday, Dec. 8, 2020, at Riverside Landing in McConnelsville.
Sharon A. Quimby
Sharon A. Quimby, 70, of Watertown, Ohio, passed away on Monday, Dec. 7, 2020, at Marietta Memorial Hospital.
Phillip C. Bowman
Phillip C. Bowman, 75, of Beaver, Ohio, formerly of Chesterhill, passed away on Tuesday, Dec. 8, 2020, at the Adena Medical Center in Chillicothe.
Sharon S. Dearth
Sharon S. Dearth, 72, of Marietta, passed away at 1 p.m. Sunday, Dec. 6, 2020, at Riverside Methodist Hospital in Columbus.
Margaret Arline (nee Golden) Bennett
Margaret Arline (nee Golden) Bennett, 75, recently of Lancaster, but long time resident of McConnelsville, Ohio, passed away Monday, Dec. 7, 2020.
Doris R. Ray
Doris R. Ray, 87, of McConnelsville, passed away Monday, Dec. 7, 2020, at the Marietta Memorial Hospital.
Mary Elizabeth Van Horn
Mary Elizabeth Van Horn, 97, of McConnelsville, formerly of Chesterhill, passed away Thursday, Dec. 3, 2020, at the Highland Oaks Health Center in McConnelsville.
Roxie J. Cordray
Roxie J. Cordray, 82, of McConnelsville, passed away at 6 p.m. Friday, Dec. 4, 2020, at Highland Oaks Health Center in McConnelsville.
William L. Bonzar
William L. Bonzar, 78, of Somerset, passed away on Friday, Nov. 27, 2020, at the Highland Oaks Nursing Home in McConnelsville.
Ralph Lee Dew
Ralph Lee Dew, 84, formerly of Zanesville, died at Highland Oaks Health Center, McConnelsville, OH, on Wednesday, Dec. 2, 2020.
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Home » Mansfield Plumbing awards four scholarships
Mansfield Plumbing awards four scholarships
Mansfield Plumbing has awarded $1,000 scholarships to four college students whose parents each work at the plumbing product manufacturer location in Perrysville, Ohio.
Kolton Edmondson, son of Rod Edmondson, the Model Shop Manager at Mansfield Plumbing, is attending Ashland University to study accounting. His father has been a valuable member of the company for 32 years.
Zackary Lane, son of Jeremy Lane, a Supervisor in the Raw Material Processing department, is enrolled at Kent State University studying pre-med and public health. His father has worked at Mansfield Plumbing for more than 20 years.
Abigail Sgro, daughter of Don Coots, a Lead/VRP in the Capillary Bench Cast department, is studying criminal justice at Ashland University. Her father has been employed with the company for more than two years.
Lauren Thompson, daughter of Raymond Thompson, a Finisher in the Bench and Battery Cast department at Mansfield Plumbing, is attending Kent State University to study nursing. Her father joined the company earlier this year.
"We've offered our scholarship program to deserving children of our employees for several years," says Natalie Thomas, vice president of human resources at Mansfield Plumbing. "We're always honored to support the educational efforts of students pursuing higher education. We hope the $4,000 being used by this year's recipients will assist them with their ongoing educational efforts for the 2015/2016 school year."
Industry Community News Wholesalers & Distributors
Assured Comfort Heating, Air & Plumbing Awards More Than $25,000 in Scholarships
Mansfield Plumbing Support for Local Organizations Grows in 2016
Mansfield Plumbing Wins 2018 GOOD DESIGN Award
Mansfield Plumbing


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text:
Alternate energy : assessment and implementation reference by James J. Winebrake
By James J. Winebrake
''Over the previous numerous years, and executive have became to a strategic making plans procedure referred to as ''roadmapping'' to aid determine destiny strength administration practices and applied sciences. This booklet considers power administration and expertise improvement over the subsequent numerous many years by way of exploring facts from those power know-how roadmaps. foreign in scope, the e-book examines either the technical and non-technical facets of rising applied sciences. certain know-how exams for particular substitute strength assets are provided. an outline of the issues linked to traditional strength intake is integrated, in addition to an insightful dialogue of expertise implementation matters from the author's personal well-informed and carefully confident perspective.''--BOOK JACKET. Read more...
Read or Download Alternate energy : assessment and implementation reference book PDF
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Additional info for Alternate energy : assessment and implementation reference book
Even more dramatic improvements—ranging from 50 to 80 percent—could be achieved with aggressive implementation of this technology roadmap, including a long-term approach to research and development. Ultimately, the appropriate use of combined heating, cooling, and power systems, optimized building controls, solar and other forms of renewable energy, and energy-efficient building shells and equipment can produce commercial buildings that become net electricity generators rather than consumers. Looking Forward How might these "healthy, productive, and desirable" commercial buildings look and perform?
Activity Time Frame ———————————————————————————————— Create educational programs on the design, installation, and use of lighting controls. S ———————————————————————————————— Improve education on daylighting, including simple rules of thumb for architects. S ———————————————————————————————— Provide training to product sales and distribution professionals about life-cycle cost analysis and the effects of lighting on people. S ———————————————————————————————— Increase information on NCQLP and CLMC certification requirements into Request for Proposal and Request for Quote language for building construction projects and energy-saving performance contracts.
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