In blongworth/HybridGIS: Data, Functions, and Analysis for the NOSAMS Hybrid Gas Ion Source
options(htmltools.dir.version = FALSE)
knitr::opts_chunk$set(echo = FALSE, message = FALSE, warning = FALSE, fig.showtext = TRUE)
# Libraries
library(tidyverse)
library(xaringanthemer)
library(here)
library(HybridGIS)
library(patchwork)
library(amstools)
library(gt)
library(knitr)
library(broom)
# Colors
dark_blue <- "#041e42"
light_blue <- "#00a9e0"
medium_blue <- "0069b1"
aqua_blue <- "#00b7bd"
cool_gray_11 <- "#53565a"
cool_gray_4 <- "#bbbcbc"
background <- "#e6e7e8"
style_mono_light(
# colors
base_color = dark_blue,
white_color = lighten_color("#d9d9d6", .5),
# primary_color = background,
# secondary_color = dark_blue,
# fonts
header_font_google = google_font("Montserrat", "800"),
text_font_google = google_font("Red Hat Display", "500")
)
style_extra_css(
css = list(
".title-slide" = list(
"background-image" = "url(images/whoi_seal_title_bkgnd.jpg)",
"background-size" = "cover"
)
),
append = TRUE,
heading = "Extra CSS"
)
How do you sputter a gas?
$$CO_2+e^- \longrightarrow C^-+2O^-$$
.pull-left[
Inject CO2 gas onto Ti target
Sputter with Cs+
Dissociate molecules
Produce C- ions (and O-, CH-, OH-, CO-...)
* Extract C- beam
]
.pull-right[
.right[Gas density in HGIS Cathode
(Salazar, 2013)]
]
???
introduction onto Ti surface in target recess
Predicted chemistry/physics of decomposition and ionization
Plasma in target recess? Blue glow.
Why run gas instead of graphite?
.pull-left[
Benefits
- Skip a time-consuming reduction to graphite
- Rapid iteration for science and development
- Eliminate blank associated with graphitization
Challenges
- Ultimate precision is lower
- Lower currents/counts (time to precision)
- Variable blank (current dependent)
- Reproducible sample performance
]
.pull-right[
methods <- tribble(~" ", ~"Graphite", ~"CFAMS ECR GIS", ~"Hybrid GIS",
"Continous flow", "No", "Yes", "No",
"Measurement time", "30 min", "5 min", "15-30 min",
"Precision", "0.3%", "1%", "1%",
"Background", "0.1 pmc", "1 pmc", "1 pmc")
methods %>%
kable(format = "html",
caption = "Comparison of AMS methods")
]
???
The NEC HGIS
.pull-left[
- Pneumatic arm brings gas to source
- Introduce gas to back of cathode
- The rest is gas handling
.center[
]
.right[MCSNICS source with gas arm]
]
.pull-right[
.center[
]
]
???
Targets
.pull-left[
Goal
- Maximize gas density and residence time where sputtering and ionization are happening
Potential for
- Better performance
- Lower cost
]
.pull-right[
.right[NEC hybrid target cutaway]
]
???
CAMS and ETH have both done a lot of target geometry research.
In the end, compromise between simplicity and function.
Maybe still gains to be made.
First tests
.pull-left[
Conditions
- Pure CO2 to source
- Modern and dead CO2
- 8 m x 50 μm capillary
- 0-4 atm supply pressure
Results
- ~8 μA 12C-
- <1% precision (SE of repeat measurements)
- 1% blank
- 1-3% total efficiency
]
.pull-right[
read.csv(here("data/14Nov2017LPtest.csv"), skip = 4) %>%
mutate(flow = flowcalc(supply/10, 2E-5, 1.49E-5, 1.58)) %>%
ggplot(aes(flow, current)) +
geom_smooth(se = FALSE, span = 0.7, color = "#00b7bd") +
geom_point(size = 4, color = "#0069b1") +
labs(title = "Ion current and gas flow",
x = bquote('CO'[2]~'Flow (μl/min)'),
y = "12C current (μA)") +
theme_classic() +
theme_xaringan(title_font_size = 20,
text_font_size = 16,
background_color = "#ffffff")
]
???
Bottle gas allowed testing full range of flows
Precision not accuracy: modern gas normalized to itself
Inlet/interface systems
.pull-left[
Adapter between CO2 producers and source.
Goal: constant flow of CO2 to source at ideal rate.
Increase CO2 flow by increasing pCO2
- Add CO2
- Reduce volume
Gas mixtures - helium dilution
- Increase pressure and total flow
- Reduce pCO2
- CO2 flow remains constant? No - viscosity
- He is 25% more viscous than CO2
]
.pull-right[
$$PV=nRT$$
$$Q=\frac{\Delta P \pi R^4}{8\mu L}$$
]
???
Pressure of CO2 in inlet controls flow in capillary
P = flow = current
1/viscosity = flow
Gas inlet controls flow of CO2 to source by varying pressure, volume, and gas composition.
increase partial pressure of CO2 by dilution?
Dilution Makes moving small amounts of gas easier by increasing pressure
Source doesn't care about helium as long as it gets its CO2
Introduce steady, reproducible current as key to data quality
Constant flow of CO2 to source produces constant currents, which reduces ratio variability.
.pull-left[
Closed inlet
Flow control
- Change volume
- Add CO2
- Add helium
For a given sample size
- High inlet volume = low inlet pressure (NEC inlet)
- Low inlet volume = high inlet pressure (Ionplus inlet)
]
.pull-right.center[
.right[Dual-bellows closed inlet]
]
???
- Mechanically more complex
- Extra sample transfer time
- but can be done during burn-in
- Data/performance simpler
Smallest samples require minimizing inlet volume and pressure while maintaining good flow control
NOSAMS closed inlet
.pull-left[
IRMS dual bellows
- VG Prism inlet
- Bellows for volume control
- Cryotraps to move and isolate CO2
- LabVIEW control
- Manifold capable
.right[Closed inlet schematic]
]
.pull-right[
data <- get_hgis_data(here("data/USAMS052318RHIGS.txt")) %>%
mutate(Num = ifelse(Pos == 15, "S", "U"))
stdrat <- mean(data$cor1412he[data$Num == "S" & data$outlier == FALSE])
data_523 <- mutate(data, normFm = norm_gas(cor1412he, stdrat))
data <- get_hgis_data(here("data/USAMS053018R.txt"), as.Date("2018-05-30")) %>%
mutate(Pos = ifelse(Sample.Name == "OX-II", 6, Pos),
Num = ifelse(Pos == 5, "S", "U"))
stdrat <- mean(data$cor1412he[data$Num == "S"])
data_530 <- mutate(data, normFm = norm_gas(cor1412he, stdrat))
data <- get_hgis_data(here("data/USAMS060618R.txt")) %>%
filter(Pos != 1, #remove spurious pos1 runs
!(Pos == 3 & X13.12he > 0.011)) %>% #remove solid OX-I fliers
mutate(Num = ifelse(Pos == 5, "S", "U"))
stdrat <- mean(data$cor1412he[data$Num == "S" & data$outlier == FALSE])
data_606 <- mutate(data, normFm = norm_gas(cor1412he, stdrat))
dbd <- rbind(data_523, data_530, data_606)
std <- getStdTable()
dbds <- sum_hgis(dbd) %>%
mutate(rec_num = case_when(str_detect(Sample.Name, "Live") ~ 101730,
Sample.Name == "OX-I" ~ 34148,
Sample.Name == "OX-II" ~ 34149,
str_starts(Sample.Name, "C1") ~ 83028)) %>%
left_join(select(std, rec_num, fm_consensus), by = "rec_num") %>%
mutate(fm_consensus = case_when(rec_num == 101730 ~ 1.0398,
rec_num == 72446 ~ 0.0013,
TRUE ~ fm_consensus),
fm_diff = mean - fm_consensus)
dbds %>%
filter(Cur < 10) %>%
ggplot(aes(fm_consensus, fm_diff)) +
geom_hline(yintercept = 0) +
geom_pointrange(aes(ymin = fm_diff - merr, ymax = fm_diff + merr),
position = position_dodge2(width = .1),
size = .6,
color = "#0069b1") +
labs(title = "Closed inlet",
x = "Expected Fm",
y = "Fm difference") +
theme_classic() +
theme_xaringan(title_font_size = 20,
text_font_size = 16,
background_color = "#ffffff")
]
.pull-left[
Open split
- Pressure is held constant at 1 atm
- Gas mixture controls pCO2
- Helium displaces CO2 from vials into split
- Capillary to source controls gas flow
Open inlets maintain constant atmospheric pressure, producing constant1 flow to source.
]
.pull-right.center[
.right[Open inlet setup]
]
.footnote[[1] flow varies by composition due to differing viscosity]
???
- Mechanically/operationally simple
- Harder to control current variability
- during run
- if sample size differs
Carbonate method
.pull-left[
Sample preparation
- Weigh carbonate into vial
- Evacuate vial
- Add acid to carbonate
- Reaction fills vial with CO2 at 1 atm
Measurement
- Pre-sputter target
- Displace sample from vial to split with He
- Measure ratios for 15-30 min.
]
.pull-right[
.center[
]
.right[Automated gas handling system (Roberts, 2013)
]
]
???
table: comparison of carbonate methods
figure: DAG graph of process
.pull-left[
Currents and flows
Helium displacement of CO2 from vial.
Monitor currents and ratios as pCO2 decreases.
- No current suppression at high pCO2
- Current stable until 40 min
- 40 min ≈ 2 mL CO2
]
.pull-right[
data <- get_hgis_data(here("data/USAMS040121R.txt"), as.Date("2021-04-02")) %>%
filter(Pos == 5) %>%
mutate(time = cum_acqtime/60,
co2flow = concCO2(time, r = 244) * 30) #30ul/min
# flow vs time subplot
flow_time <- data.frame(x = 0:125) %>%
ggplot(aes(x)) +
stat_function(fun=function(x) concCO2(x, r = 244) * 30, aes(color = "CO2"), size = 1.5) +
stat_function(fun=function(x) 30 - (concCO2(x, r = 244) * 30), aes(color = "Helium"), size = 1.5) +
scale_color_manual("Gas", values = c("#00b7bd", "#b7bf10")) +
labs(title = "Gas flows to source",
subtitle = "7mL vial, 244μL/min helium, 30μL/min to source",
y = "Gas flow (μL/min)") +
theme_classic() +
theme_xaringan(title_font_size = 20,
text_font_size = 18,
background_color = "#ffffff") +
theme(axis.title.x = element_blank(),
axis.text.x = element_blank(),
legend.position = c(0.87, 0.65),
legend.background = element_rect(fill = "white", color = "black"))
# current vs time subplot
cur_time <- ggplot(data, aes(time, he12C)) +
geom_smooth(span = .4, se=FALSE, color = "#00b7bd") +
geom_point(size = 3, color = "#0069b1") +
xlim(0, 125) +
labs(title = "Ion current",
x = "Time (min)",
y = "12C current (μA)") +
theme_classic() +
theme_xaringan(title_font_size = 20,
text_font_size = 18,
background_color = "#ffffff")
flow_time / cur_time
]
.pull-left[
Currents and flows
- Note similarity to first tests
- Flat top = stable currents/ratios
- Sample size can be reduced to 2 mL (~1 mg C)
- Further reduction with increased flow to source
Balance flow rates to maximize stable currents and minimize sample size
]
.pull-right[
ggplot(data, aes(co2flow, he12C)) +
geom_smooth(span = .3, se=FALSE, color = "#00b7bd") +
geom_point(size = 3, color = "#0069b1") +
labs(title = "Vial dilution current",
subtitle = "250μL/min displacement, 30μl/min delivery",
x = bquote('CO'[2]~'Flow (μl/min)'),
y = "12C current (μA)") +
theme_classic() +
theme_xaringan(title_font_size = 20,
text_font_size = 16,
background_color = "#ffffff")
]
???
compare to first tests
Note that our flat-topped current curve allows variable flow of the vial system. MICADAS has a more pronounced peak so this may not work as well. (Check papers for peak shape)
Also note current dependent ratio (blank) as a complicating factor
Lead into blank on next slide
data<- get_hgis_data(here("data/USAMS101320R.txt"), as.Date("2020-11-17")) %>%
mutate(Num = ifelse(Pos %in% c(2, 4), "S", ifelse(Num == "S", "U", Num)))
stdrat <- mean(data$cor1412he[data$Num == "S" & !data$outlier])
data1013 <- data %>% mutate(normFm = norm_gas(cor1412he, stdrat))
data <- get_hgis_data(here("data/USAMS120320R.txt"), as.Date("2020-12-04")) %>%
mutate(Num = ifelse(Pos %in% c(2, 4), "S", ifelse(Num == "S", "U", Num)))
stdrat <- mean(data$cor1412he[data$Num == "S" & !data$outlier])
data1204 <- data %>% mutate(normFm = norm_gas(cor1412he, stdrat))
data1211 <- get_hgis_data(here("data/USAMS120320R.txt")) %>%
filter(Pos %in% 1:4 | as.Date(ts) == "2020-12-11")
data <- get_hgis_data(here("data/USAMS120320R.txt"), as.Date("2020-12-18")) %>%
filter(Pos != 0) %>%
mutate(Num = ifelse(Pos %in% c(2, 4), "S", ifelse(Num == "S", "U", Num)))
stdrat <- mean(data$cor1412he[data$Num == "S" & !data$outlier])
data1218 <- data %>% mutate(normFm = norm_gas(cor1412he, stdrat))
data0108 <- get_hgis_data(here("data/USAMS120320R.txt"), as.Date("2021-01-08"))
data <- get_hgis_data(here("data/USAMS020521R.txt")) %>%
mutate(Num = ifelse(Pos %in% c(2, 4), "S", ifelse(Num == "S", "U", Num)))
stdrat <- mean(data$cor1412he[data$Num == "S" & !data$outlier])
data205 <- data %>% mutate(normFm = norm_gas(cor1412he, stdrat))
data <- get_hgis_data(here("data/USAMS030421R.txt"), as.Date("2021-03-05")) %>%
mutate(Num = ifelse(Pos %in% c(2, 4), "S", ifelse(Num == "S", "U", Num)))
stdrat <- mean(data$cor1412he[data$Num == "S" & !data$outlier])
data304 <- data %>% mutate(normFm = norm_gas(cor1412he, stdrat))
data <- get_hgis_data(here("data/USAMS040121R.txt"), as.Date("2021-04-09")) %>%
mutate(Num = ifelse(Pos %in% c(22, 24), "S", ifelse(Num == "S", "U", Num)))
stdrat <- mean(data$cor1412he[data$Num == "S" & !data$outlier])
data409 <- data %>% mutate(normFm = norm_gas(cor1412he, stdrat))
data <- get_hgis_data(here("data/USAMS041521R.txt"), as.Date("2021-04-16")) %>%
mutate(Num = ifelse(Pos %in% c(22, 24), "S", ifelse(Num == "S", "U", Num)))
stdrat <- mean(data$cor1412he[data$Num == "S" & !data$outlier])
data415 <- data %>% mutate(normFm = norm_gas(cor1412he, stdrat))
std <- getStdTable()
data <- rbind(data1013, data1204, data1211, data1218, data205, data304, data409, data415) %>%
mutate(rec_num = case_when(str_detect(Sample.Name, "LiveGas") ~ 101730,
str_starts(Sample.Name, "C-1") ~ 83028,
str_starts(Sample.Name, "C1") ~ 83028,
str_starts(Sample.Name, "TIRI-F") ~ 2138,
str_starts(Sample.Name, "TIRI-I") ~ 17185,
str_starts(Sample.Name, "C-2") ~ 1082,
str_starts(Sample.Name, "C2") ~ 1082,
str_starts(Sample.Name, "NOSAMS") ~ 38809,
str_detect(Sample.Name, "DeadGas") ~ 72446)) %>%
left_join(select(std, rec_num, fm_consensus), by = "rec_num") %>%
mutate(fm_consensus = case_when(rec_num == 101730 ~ 1.0398,
rec_num == 72446 ~ 0.0013,
TRUE ~ fm_consensus))
# Summarize by target
ds <- sum_hgis(data) %>%
filter((str_detect(Sample.Name, "LiveGas") & Cur > 1) |
(str_detect(Sample.Name, "DeadGas") & Cur > 1) |
str_detect(Sample.Name, "Blank")) %>%
mutate(Gas = case_when(str_detect(Sample.Name, "LiveGas") ~ "LiveGas",
str_detect(Sample.Name, "DeadGas") ~ "DeadGas",
TRUE ~ "Blank"),
Cur_inv = 1/Cur)
# Fit linear model
fits <- ds %>%
ungroup() %>%
nest(data = -Gas) %>%
mutate(fit = map(data, ~lm(mean ~ Cur_inv, data = .x)),
tidied = map(fit, tidy)) %>%
unnest(tidied)
blankfit <- fits %>%
select(Gas, term, estimate) %>%
pivot_wider(names_from = c(Gas, term), values_from = estimate) %>%
mutate(inv_m_blank = -(`DeadGas_(Intercept)` - `LiveGas_(Intercept)`)/(DeadGas_Cur_inv - LiveGas_Cur_inv),
Fm_blank = `DeadGas_(Intercept)` + DeadGas_Cur_inv * inv_m_blank,
m_blank = 1/inv_m_blank)
# Modeled Fm and mass of blank
blank_val <- blankfit %>%
select(Fm_blank, m_blank)
.pull-left[
Current dependent blank
$$I_{sample} + I_{blank} = I_{meas}$$
$$Fm_mI_m=Fm_bI_b+Fm_sI_s$$
$$Fm_s = Fm_m + \frac{I_b(Fm_m-Fm_b)}{I_m-I_b}$$
0.1 μA blank current with 10 μA total = 1% blank
.pull-left[
Modeled blank
- Fm:
r format(blank_val$Fm_blank, digits = 3)
- Current:
r format(blank_val$m_blank, digits = 3) μA
]
.pull-right[
Measured blank
- Fm:
r ds %>% filter(Gas == "Blank") %>% pull(mean) %>% mean() %>% format(digits = 3)
- Current:
r ds %>% filter(Gas == "Blank") %>% pull(Cur) %>% mean() %>% format(digits = 3) μA
]
]
.pull-right[
xl <- 8
livefm_cur <- data %>%
filter(!(he12C < 2.5 & normFm > 1.05) & fm_consensus > 0.9) %>%
ggplot(aes(he12C, normFm)) +
geom_point(size = 2, color = "#0069b1") +
xlim(0, xl) +
ylim(.8, 1.2) +
ggtitle("Ratio vs current") +
theme_classic() +
theme_xaringan(title_font_size = 20,
text_font_size = 18,
background_color = "#ffffff") +
theme(axis.title.x = element_blank(),
axis.title.y = element_blank(),
axis.text.x = element_blank())
deadfm_cur <- data %>%
filter(fm_consensus < 0.1 & normFm < 0.3 & Sample.Name != "DilDeadGas6") %>%
ggplot(aes(he12C, normFm)) +
geom_point(size = 2, color = "#00a9e0") +
xlim(0, xl) +
ylim(0, .4) +
xlab("12C Current (μA)") +
theme_classic() +
theme_xaringan(title_font_size = 20,
text_font_size = 18,
background_color = "#ffffff")
(livefm_cur / deadfm_cur) +
ylab("Fraction modern") +
theme(axis.title.y = element_text(hjust=-1))
# Plot of data and model with measured blanks
ggplot(ds, aes(Cur_inv, mean, color = Gas)) +
geom_abline(slope = blankfit$LiveGas_Cur_inv,
intercept = blankfit$`LiveGas_(Intercept)`,
color = "#0069b1") +
geom_abline(slope = blankfit$DeadGas_Cur_inv,
intercept = blankfit$`DeadGas_(Intercept)`,
color = "#00a9e0") +
geom_pointrange(aes(ymin = mean - merr, ymax = mean + merr), size = .3) +
scale_color_manual("Gas", values = c("#b7bf10", "#00a9e0", "#0069b1")) +
xlim(0, 10) +
labs(title = "Modeled blank",
subtitle = "Fits cross at Fm and current of blank",
x = "Inverse 12C Current (μA-1)",
y = "Fraction modern") +
theme_classic() +
theme_xaringan(title_font_size = 20,
text_font_size = 16,
background_color = "#ffffff") +
theme(legend.position = "none",
legend.background = element_rect(fill = "white", color = "black"))
]
???
Blank = Ion current not from sample gas
Targets alone and targets + helium (fm and current)
...but if current is kept constant, current drops out of correction
carb_data <- map_dfr(list.files(here("data_analysed"), full.names = TRUE), read_csv) %>%
filter(!is.na(rec_num),
Cur > 2) %>%
mutate(fm_diff = mean - fm_consensus,
fm_sigma = sigma(mean, fm_consensus, merr),
fmbc_diff = fm_lbc - fm_consensus,
fmbc_sigma = sigma(fm_lbc, fm_consensus, merr))
Current independent blank
.pull-left[
If standards, blanks, and unknowns have the same blank and current, current drops out.
Linear fit used to correct:
$$Fm_s = Fm_m - Fm_{blk}\frac{Fm_{std} - Fm_m}{Fm_{std}}$$
Mean Fm of blanks: r carb_data %>% filter(fm_consensus < 0.1 & Cur > 2) %>% pull(mean) %>% mean() %>% format(digits = 3)
]
.pull-right[
carb_data %>% filter(fm_consensus < 0.1 &
Cur > 2) %>%
ggplot(aes(Cur, mean)) +
geom_errorbar(aes(ymin = mean - merr, ymax = mean + merr)) +
geom_errorbarh(aes(xmin = Cur - Cur.sd, xmax = Cur + Cur.sd)) +
geom_point(aes(color = wheel), size = 3) +
scale_color_manual("wheel", values = c("#b7bf10", "#00a9e0", "#0069b1")) +
labs(title = "Blanks",
x = "12C Current (μA)",
y = "Fraction modern") +
theme_classic() +
theme_xaringan(title_font_size = 20,
text_font_size = 16,
background_color = "#ffffff") +
theme(legend.position = c(0.82, 0.76),
legend.background = element_rect(fill = "white", color = "black"))
]
???
We'll see what fit looks like on next slide.
Current independent correction
Current/target dependent?
Constant blanks and currents allow single mass independent correction
3 ways to control blank
Reduce blank - better cleaning, presputtering
Hold standard, blank, unknown current constant and use current independent correction (linear correction based on dead sample Fm)
* Determine Fm and current of blank and use current dependent correction (current balance model)
Idea: use data from dillution runs of live and dead gas to create curves
It's relatively easy to produce runs with terrible current, but usually not good for blank as caused by other issues.
.left-column[
Secondary standards
- Currents > 2 μA
- Current dependence
- Underestimation of error
Data summary
- Mean difference:
r format(mean(carb_data$fmbc_diff), digits = 1)
- Standard deviation of difference:
r format(sd(carb_data$fmbc_diff), digits = 2)
]
.right-column[
.right[
ps <- 0.3
dw <- 0.03
yl <- .1
secs <- ggplot(carb_data, aes(fm_consensus, fm_diff, color = Cur)) +
geom_hline(yintercept = 0) +
geom_smooth(method = "lm", se = FALSE) +
geom_pointrange(aes(ymin = fm_diff - merr, ymax = fm_diff + merr),
position = position_dodge2(width = dw),
size = ps,) +
lims(x = c(-0.05, 1.1),
y = c(-yl, yl)) +
labs(title = "Difference from expected Fm",
subtitle = "Uncorrected",
colour = "Cur (μA)",
y = "Fm difference") +
theme_classic() +
theme_xaringan(title_font_size = 18,
text_font_size = 16,
background_color = "#ffffff") +
theme(axis.title.x = element_blank(),
axis.text.x = element_blank(),
legend.direction = "horizontal",
legend.position = c(.80, 1),
legend.justification = "bottom")
secs_lbc <- ggplot(carb_data, aes(fm_consensus, fmbc_diff, color = Cur)) +
geom_hline(yintercept = 0) +
geom_smooth(method = "lm", se = FALSE) +
geom_pointrange(aes(ymin = fmbc_diff - merr, ymax = fmbc_diff + merr),
position = position_dodge2(width = dw),
size = ps) +
lims(x = c(-0.05, 1.1),
y = c(-yl, yl)) +
labs(subtitle = "Blank corrected",
x = "Fm expected",
y = "Fm difference") +
theme_classic() +
theme_xaringan(title_font_size = 18,
text_font_size = 16,
background_color = "#ffffff") +
theme(legend.position = "None")
secs / secs_lbc
]
]
???
precision, accuracy
issues with blank and normalization
MICADAS
.pull-left[
Coming soon(ish)...
Source differences
- One target in source at a time - lower blank
- Gas supplied via conical joint in side of target
Inlet differences
- MICADAS uses closed inlet
- CO2 from carbonates transferred onto resin trap in stream of helium
- Sample introduced by adding helium and adjusting flow using variable volume
]
.pull-right[
.right[MICADAS Gas Interface]
]
???
More pronounced current vs. flow curve makes measurement more difficult at variable CO2 flow.
Salazar and Szidat are developing an open split interface with EA - squish peak with dead volume
photo: micadas source
Summary
NOSAMS has developed a successful method for analyzing radiocarbon in carbonates with an open-split interface delivering CO2 gas to a NEC MCSNICS gas-accepting cesium sputter ion source.
Goals:
- Finish characterization of the method
- Compare to graphite with test samples
- Improve accuracy
- Better understand method blank
- Reduce sample size
- Automate data acquisition and reduction
Thank You!
Mark Roberts, Josh Burton, Josh Hlavenka, Al Gagnon, Kalina Gospodinova, Mark Kurz, and the staff of NOSAMS
Karl von Reden
Cam McIntyre, SUERC
Ted Ognibene, CAMS
blongworth/HybridGIS documentation built on Dec. 19, 2021, 10:41 a.m.
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options(htmltools.dir.version = FALSE) knitr::opts_chunk$set(echo = FALSE, message = FALSE, warning = FALSE, fig.showtext = TRUE) # Libraries library(tidyverse) library(xaringanthemer) library(here) library(HybridGIS) library(patchwork) library(amstools) library(gt) library(knitr) library(broom) # Colors dark_blue <- "#041e42" light_blue <- "#00a9e0" medium_blue <- "0069b1" aqua_blue <- "#00b7bd" cool_gray_11 <- "#53565a" cool_gray_4 <- "#bbbcbc" background <- "#e6e7e8" style_mono_light( # colors base_color = dark_blue, white_color = lighten_color("#d9d9d6", .5), # primary_color = background, # secondary_color = dark_blue, # fonts header_font_google = google_font("Montserrat", "800"), text_font_google = google_font("Red Hat Display", "500") ) style_extra_css( css = list( ".title-slide" = list( "background-image" = "url(images/whoi_seal_title_bkgnd.jpg)", "background-size" = "cover" ) ), append = TRUE, heading = "Extra CSS" )
How do you sputter a gas?
$$CO_2+e^- \longrightarrow C^-+2O^-$$
.pull-left[ Inject CO2 gas onto Ti target Sputter with Cs+ Dissociate molecules Produce C- ions (and O-, CH-, OH-, CO-...) * Extract C- beam ]
.pull-right[
.right[Gas density in HGIS Cathode
(Salazar, 2013)] ]
???
introduction onto Ti surface in target recess
Predicted chemistry/physics of decomposition and ionization
Plasma in target recess? Blue glow.
Why run gas instead of graphite?
.pull-left[
Benefits
- Skip a time-consuming reduction to graphite
- Rapid iteration for science and development
- Eliminate blank associated with graphitization
Challenges
- Ultimate precision is lower
- Lower currents/counts (time to precision)
- Variable blank (current dependent)
- Reproducible sample performance ]
.pull-right[
methods <- tribble(~" ", ~"Graphite", ~"CFAMS ECR GIS", ~"Hybrid GIS", "Continous flow", "No", "Yes", "No", "Measurement time", "30 min", "5 min", "15-30 min", "Precision", "0.3%", "1%", "1%", "Background", "0.1 pmc", "1 pmc", "1 pmc") methods %>% kable(format = "html", caption = "Comparison of AMS methods")
]
???
The NEC HGIS
.pull-left[
- Pneumatic arm brings gas to source
- Introduce gas to back of cathode
- The rest is gas handling
.center[]
.right[MCSNICS source with gas arm]
]
.pull-right[
.center[
]
]
???
Targets
.pull-left[
Goal
- Maximize gas density and residence time where sputtering and ionization are happening
Potential for
- Better performance
- Lower cost ]
.pull-right[
.right[NEC hybrid target cutaway]
]
???
CAMS and ETH have both done a lot of target geometry research.
In the end, compromise between simplicity and function.
Maybe still gains to be made.
First tests
.pull-left[
Conditions
- Pure CO2 to source
- Modern and dead CO2
- 8 m x 50 μm capillary
- 0-4 atm supply pressure
Results
- ~8 μA 12C-
- <1% precision (SE of repeat measurements)
- 1% blank
- 1-3% total efficiency ]
.pull-right[
read.csv(here("data/14Nov2017LPtest.csv"), skip = 4) %>% mutate(flow = flowcalc(supply/10, 2E-5, 1.49E-5, 1.58)) %>% ggplot(aes(flow, current)) + geom_smooth(se = FALSE, span = 0.7, color = "#00b7bd") + geom_point(size = 4, color = "#0069b1") + labs(title = "Ion current and gas flow", x = bquote('CO'[2]~'Flow (μl/min)'), y = "12C current (μA)") + theme_classic() + theme_xaringan(title_font_size = 20, text_font_size = 16, background_color = "#ffffff")
]
???
Bottle gas allowed testing full range of flows
Precision not accuracy: modern gas normalized to itself
Inlet/interface systems
.pull-left[ Adapter between CO2 producers and source.
Goal: constant flow of CO2 to source at ideal rate.
Increase CO2 flow by increasing pCO2
- Add CO2
- Reduce volume
Gas mixtures - helium dilution
- Increase pressure and total flow
- Reduce pCO2
- CO2 flow remains constant? No - viscosity
- He is 25% more viscous than CO2 ]
.pull-right[
$$PV=nRT$$
$$Q=\frac{\Delta P \pi R^4}{8\mu L}$$
]
???
Pressure of CO2 in inlet controls flow in capillary
P = flow = current
1/viscosity = flow
Gas inlet controls flow of CO2 to source by varying pressure, volume, and gas composition.
increase partial pressure of CO2 by dilution?
Dilution Makes moving small amounts of gas easier by increasing pressure
Source doesn't care about helium as long as it gets its CO2
Introduce steady, reproducible current as key to data quality
Constant flow of CO2 to source produces constant currents, which reduces ratio variability.
.pull-left[
Closed inlet
Flow control
- Change volume
- Add CO2
- Add helium
For a given sample size
- High inlet volume = low inlet pressure (NEC inlet)
- Low inlet volume = high inlet pressure (Ionplus inlet)
]
.pull-right.center[
.right[Dual-bellows closed inlet] ]
???
- Mechanically more complex
- Extra sample transfer time
- but can be done during burn-in
- Data/performance simpler
Smallest samples require minimizing inlet volume and pressure while maintaining good flow control
NOSAMS closed inlet
.pull-left[
IRMS dual bellows
- VG Prism inlet
- Bellows for volume control
- Cryotraps to move and isolate CO2
- LabVIEW control
- Manifold capable
.right[Closed inlet schematic]
]
.pull-right[
data <- get_hgis_data(here("data/USAMS052318RHIGS.txt")) %>% mutate(Num = ifelse(Pos == 15, "S", "U")) stdrat <- mean(data$cor1412he[data$Num == "S" & data$outlier == FALSE]) data_523 <- mutate(data, normFm = norm_gas(cor1412he, stdrat)) data <- get_hgis_data(here("data/USAMS053018R.txt"), as.Date("2018-05-30")) %>% mutate(Pos = ifelse(Sample.Name == "OX-II", 6, Pos), Num = ifelse(Pos == 5, "S", "U")) stdrat <- mean(data$cor1412he[data$Num == "S"]) data_530 <- mutate(data, normFm = norm_gas(cor1412he, stdrat)) data <- get_hgis_data(here("data/USAMS060618R.txt")) %>% filter(Pos != 1, #remove spurious pos1 runs !(Pos == 3 & X13.12he > 0.011)) %>% #remove solid OX-I fliers mutate(Num = ifelse(Pos == 5, "S", "U")) stdrat <- mean(data$cor1412he[data$Num == "S" & data$outlier == FALSE]) data_606 <- mutate(data, normFm = norm_gas(cor1412he, stdrat)) dbd <- rbind(data_523, data_530, data_606) std <- getStdTable() dbds <- sum_hgis(dbd) %>% mutate(rec_num = case_when(str_detect(Sample.Name, "Live") ~ 101730, Sample.Name == "OX-I" ~ 34148, Sample.Name == "OX-II" ~ 34149, str_starts(Sample.Name, "C1") ~ 83028)) %>% left_join(select(std, rec_num, fm_consensus), by = "rec_num") %>% mutate(fm_consensus = case_when(rec_num == 101730 ~ 1.0398, rec_num == 72446 ~ 0.0013, TRUE ~ fm_consensus), fm_diff = mean - fm_consensus) dbds %>% filter(Cur < 10) %>% ggplot(aes(fm_consensus, fm_diff)) + geom_hline(yintercept = 0) + geom_pointrange(aes(ymin = fm_diff - merr, ymax = fm_diff + merr), position = position_dodge2(width = .1), size = .6, color = "#0069b1") + labs(title = "Closed inlet", x = "Expected Fm", y = "Fm difference") + theme_classic() + theme_xaringan(title_font_size = 20, text_font_size = 16, background_color = "#ffffff")
]
.pull-left[
Open split
- Pressure is held constant at 1 atm
- Gas mixture controls pCO2
- Helium displaces CO2 from vials into split
- Capillary to source controls gas flow
Open inlets maintain constant atmospheric pressure, producing constant1 flow to source.
]
.pull-right.center[
.right[Open inlet setup]
]
.footnote[[1] flow varies by composition due to differing viscosity]
???
- Mechanically/operationally simple
- Harder to control current variability
- during run
- if sample size differs
Carbonate method
.pull-left[
Sample preparation
- Weigh carbonate into vial
- Evacuate vial
- Add acid to carbonate
- Reaction fills vial with CO2 at 1 atm
Measurement
- Pre-sputter target
- Displace sample from vial to split with He
- Measure ratios for 15-30 min.
]
.pull-right[
.center[]
.right[Automated gas handling system (Roberts, 2013)
]
]
???
table: comparison of carbonate methods figure: DAG graph of process
.pull-left[
Currents and flows
Helium displacement of CO2 from vial.
Monitor currents and ratios as pCO2 decreases.
- No current suppression at high pCO2
- Current stable until 40 min
- 40 min ≈ 2 mL CO2 ]
.pull-right[
data <- get_hgis_data(here("data/USAMS040121R.txt"), as.Date("2021-04-02")) %>% filter(Pos == 5) %>% mutate(time = cum_acqtime/60, co2flow = concCO2(time, r = 244) * 30) #30ul/min # flow vs time subplot flow_time <- data.frame(x = 0:125) %>% ggplot(aes(x)) + stat_function(fun=function(x) concCO2(x, r = 244) * 30, aes(color = "CO2"), size = 1.5) + stat_function(fun=function(x) 30 - (concCO2(x, r = 244) * 30), aes(color = "Helium"), size = 1.5) + scale_color_manual("Gas", values = c("#00b7bd", "#b7bf10")) + labs(title = "Gas flows to source", subtitle = "7mL vial, 244μL/min helium, 30μL/min to source", y = "Gas flow (μL/min)") + theme_classic() + theme_xaringan(title_font_size = 20, text_font_size = 18, background_color = "#ffffff") + theme(axis.title.x = element_blank(), axis.text.x = element_blank(), legend.position = c(0.87, 0.65), legend.background = element_rect(fill = "white", color = "black")) # current vs time subplot cur_time <- ggplot(data, aes(time, he12C)) + geom_smooth(span = .4, se=FALSE, color = "#00b7bd") + geom_point(size = 3, color = "#0069b1") + xlim(0, 125) + labs(title = "Ion current", x = "Time (min)", y = "12C current (μA)") + theme_classic() + theme_xaringan(title_font_size = 20, text_font_size = 18, background_color = "#ffffff") flow_time / cur_time
]
.pull-left[
Currents and flows
- Note similarity to first tests
- Flat top = stable currents/ratios
- Sample size can be reduced to 2 mL (~1 mg C)
- Further reduction with increased flow to source
Balance flow rates to maximize stable currents and minimize sample size
]
.pull-right[
ggplot(data, aes(co2flow, he12C)) + geom_smooth(span = .3, se=FALSE, color = "#00b7bd") + geom_point(size = 3, color = "#0069b1") + labs(title = "Vial dilution current", subtitle = "250μL/min displacement, 30μl/min delivery", x = bquote('CO'[2]~'Flow (μl/min)'), y = "12C current (μA)") + theme_classic() + theme_xaringan(title_font_size = 20, text_font_size = 16, background_color = "#ffffff")
]
???
compare to first tests
Note that our flat-topped current curve allows variable flow of the vial system. MICADAS has a more pronounced peak so this may not work as well. (Check papers for peak shape)
Also note current dependent ratio (blank) as a complicating factor
Lead into blank on next slide
data<- get_hgis_data(here("data/USAMS101320R.txt"), as.Date("2020-11-17")) %>% mutate(Num = ifelse(Pos %in% c(2, 4), "S", ifelse(Num == "S", "U", Num))) stdrat <- mean(data$cor1412he[data$Num == "S" & !data$outlier]) data1013 <- data %>% mutate(normFm = norm_gas(cor1412he, stdrat)) data <- get_hgis_data(here("data/USAMS120320R.txt"), as.Date("2020-12-04")) %>% mutate(Num = ifelse(Pos %in% c(2, 4), "S", ifelse(Num == "S", "U", Num))) stdrat <- mean(data$cor1412he[data$Num == "S" & !data$outlier]) data1204 <- data %>% mutate(normFm = norm_gas(cor1412he, stdrat)) data1211 <- get_hgis_data(here("data/USAMS120320R.txt")) %>% filter(Pos %in% 1:4 | as.Date(ts) == "2020-12-11") data <- get_hgis_data(here("data/USAMS120320R.txt"), as.Date("2020-12-18")) %>% filter(Pos != 0) %>% mutate(Num = ifelse(Pos %in% c(2, 4), "S", ifelse(Num == "S", "U", Num))) stdrat <- mean(data$cor1412he[data$Num == "S" & !data$outlier]) data1218 <- data %>% mutate(normFm = norm_gas(cor1412he, stdrat)) data0108 <- get_hgis_data(here("data/USAMS120320R.txt"), as.Date("2021-01-08")) data <- get_hgis_data(here("data/USAMS020521R.txt")) %>% mutate(Num = ifelse(Pos %in% c(2, 4), "S", ifelse(Num == "S", "U", Num))) stdrat <- mean(data$cor1412he[data$Num == "S" & !data$outlier]) data205 <- data %>% mutate(normFm = norm_gas(cor1412he, stdrat)) data <- get_hgis_data(here("data/USAMS030421R.txt"), as.Date("2021-03-05")) %>% mutate(Num = ifelse(Pos %in% c(2, 4), "S", ifelse(Num == "S", "U", Num))) stdrat <- mean(data$cor1412he[data$Num == "S" & !data$outlier]) data304 <- data %>% mutate(normFm = norm_gas(cor1412he, stdrat)) data <- get_hgis_data(here("data/USAMS040121R.txt"), as.Date("2021-04-09")) %>% mutate(Num = ifelse(Pos %in% c(22, 24), "S", ifelse(Num == "S", "U", Num))) stdrat <- mean(data$cor1412he[data$Num == "S" & !data$outlier]) data409 <- data %>% mutate(normFm = norm_gas(cor1412he, stdrat)) data <- get_hgis_data(here("data/USAMS041521R.txt"), as.Date("2021-04-16")) %>% mutate(Num = ifelse(Pos %in% c(22, 24), "S", ifelse(Num == "S", "U", Num))) stdrat <- mean(data$cor1412he[data$Num == "S" & !data$outlier]) data415 <- data %>% mutate(normFm = norm_gas(cor1412he, stdrat)) std <- getStdTable() data <- rbind(data1013, data1204, data1211, data1218, data205, data304, data409, data415) %>% mutate(rec_num = case_when(str_detect(Sample.Name, "LiveGas") ~ 101730, str_starts(Sample.Name, "C-1") ~ 83028, str_starts(Sample.Name, "C1") ~ 83028, str_starts(Sample.Name, "TIRI-F") ~ 2138, str_starts(Sample.Name, "TIRI-I") ~ 17185, str_starts(Sample.Name, "C-2") ~ 1082, str_starts(Sample.Name, "C2") ~ 1082, str_starts(Sample.Name, "NOSAMS") ~ 38809, str_detect(Sample.Name, "DeadGas") ~ 72446)) %>% left_join(select(std, rec_num, fm_consensus), by = "rec_num") %>% mutate(fm_consensus = case_when(rec_num == 101730 ~ 1.0398, rec_num == 72446 ~ 0.0013, TRUE ~ fm_consensus)) # Summarize by target ds <- sum_hgis(data) %>% filter((str_detect(Sample.Name, "LiveGas") & Cur > 1) | (str_detect(Sample.Name, "DeadGas") & Cur > 1) | str_detect(Sample.Name, "Blank")) %>% mutate(Gas = case_when(str_detect(Sample.Name, "LiveGas") ~ "LiveGas", str_detect(Sample.Name, "DeadGas") ~ "DeadGas", TRUE ~ "Blank"), Cur_inv = 1/Cur) # Fit linear model fits <- ds %>% ungroup() %>% nest(data = -Gas) %>% mutate(fit = map(data, ~lm(mean ~ Cur_inv, data = .x)), tidied = map(fit, tidy)) %>% unnest(tidied) blankfit <- fits %>% select(Gas, term, estimate) %>% pivot_wider(names_from = c(Gas, term), values_from = estimate) %>% mutate(inv_m_blank = -(`DeadGas_(Intercept)` - `LiveGas_(Intercept)`)/(DeadGas_Cur_inv - LiveGas_Cur_inv), Fm_blank = `DeadGas_(Intercept)` + DeadGas_Cur_inv * inv_m_blank, m_blank = 1/inv_m_blank) # Modeled Fm and mass of blank blank_val <- blankfit %>% select(Fm_blank, m_blank)
.pull-left[
Current dependent blank
$$I_{sample} + I_{blank} = I_{meas}$$ $$Fm_mI_m=Fm_bI_b+Fm_sI_s$$
$$Fm_s = Fm_m + \frac{I_b(Fm_m-Fm_b)}{I_m-I_b}$$
0.1 μA blank current with 10 μA total = 1% blank
.pull-left[
Modeled blank
- Fm:
r format(blank_val$Fm_blank, digits = 3) - Current:
r format(blank_val$m_blank, digits = 3)μA ]
.pull-right[
Measured blank
- Fm:
r ds %>% filter(Gas == "Blank") %>% pull(mean) %>% mean() %>% format(digits = 3) - Current:
r ds %>% filter(Gas == "Blank") %>% pull(Cur) %>% mean() %>% format(digits = 3)μA ] ]
.pull-right[
xl <- 8 livefm_cur <- data %>% filter(!(he12C < 2.5 & normFm > 1.05) & fm_consensus > 0.9) %>% ggplot(aes(he12C, normFm)) + geom_point(size = 2, color = "#0069b1") + xlim(0, xl) + ylim(.8, 1.2) + ggtitle("Ratio vs current") + theme_classic() + theme_xaringan(title_font_size = 20, text_font_size = 18, background_color = "#ffffff") + theme(axis.title.x = element_blank(), axis.title.y = element_blank(), axis.text.x = element_blank()) deadfm_cur <- data %>% filter(fm_consensus < 0.1 & normFm < 0.3 & Sample.Name != "DilDeadGas6") %>% ggplot(aes(he12C, normFm)) + geom_point(size = 2, color = "#00a9e0") + xlim(0, xl) + ylim(0, .4) + xlab("12C Current (μA)") + theme_classic() + theme_xaringan(title_font_size = 20, text_font_size = 18, background_color = "#ffffff") (livefm_cur / deadfm_cur) + ylab("Fraction modern") + theme(axis.title.y = element_text(hjust=-1))
# Plot of data and model with measured blanks ggplot(ds, aes(Cur_inv, mean, color = Gas)) + geom_abline(slope = blankfit$LiveGas_Cur_inv, intercept = blankfit$`LiveGas_(Intercept)`, color = "#0069b1") + geom_abline(slope = blankfit$DeadGas_Cur_inv, intercept = blankfit$`DeadGas_(Intercept)`, color = "#00a9e0") + geom_pointrange(aes(ymin = mean - merr, ymax = mean + merr), size = .3) + scale_color_manual("Gas", values = c("#b7bf10", "#00a9e0", "#0069b1")) + xlim(0, 10) + labs(title = "Modeled blank", subtitle = "Fits cross at Fm and current of blank", x = "Inverse 12C Current (μA-1)", y = "Fraction modern") + theme_classic() + theme_xaringan(title_font_size = 20, text_font_size = 16, background_color = "#ffffff") + theme(legend.position = "none", legend.background = element_rect(fill = "white", color = "black"))
]
???
Blank = Ion current not from sample gas
Targets alone and targets + helium (fm and current)
...but if current is kept constant, current drops out of correction
carb_data <- map_dfr(list.files(here("data_analysed"), full.names = TRUE), read_csv) %>% filter(!is.na(rec_num), Cur > 2) %>% mutate(fm_diff = mean - fm_consensus, fm_sigma = sigma(mean, fm_consensus, merr), fmbc_diff = fm_lbc - fm_consensus, fmbc_sigma = sigma(fm_lbc, fm_consensus, merr))
Current independent blank
.pull-left[ If standards, blanks, and unknowns have the same blank and current, current drops out.
Linear fit used to correct:
$$Fm_s = Fm_m - Fm_{blk}\frac{Fm_{std} - Fm_m}{Fm_{std}}$$
Mean Fm of blanks: r carb_data %>% filter(fm_consensus < 0.1 & Cur > 2) %>% pull(mean) %>% mean() %>% format(digits = 3)
]
.pull-right[
carb_data %>% filter(fm_consensus < 0.1 & Cur > 2) %>% ggplot(aes(Cur, mean)) + geom_errorbar(aes(ymin = mean - merr, ymax = mean + merr)) + geom_errorbarh(aes(xmin = Cur - Cur.sd, xmax = Cur + Cur.sd)) + geom_point(aes(color = wheel), size = 3) + scale_color_manual("wheel", values = c("#b7bf10", "#00a9e0", "#0069b1")) + labs(title = "Blanks", x = "12C Current (μA)", y = "Fraction modern") + theme_classic() + theme_xaringan(title_font_size = 20, text_font_size = 16, background_color = "#ffffff") + theme(legend.position = c(0.82, 0.76), legend.background = element_rect(fill = "white", color = "black"))
]
???
We'll see what fit looks like on next slide.
Current independent correction
Current/target dependent? Constant blanks and currents allow single mass independent correction
3 ways to control blank
Reduce blank - better cleaning, presputtering
Hold standard, blank, unknown current constant and use current independent correction (linear correction based on dead sample Fm)
* Determine Fm and current of blank and use current dependent correction (current balance model)
Idea: use data from dillution runs of live and dead gas to create curves
It's relatively easy to produce runs with terrible current, but usually not good for blank as caused by other issues.
.left-column[
Secondary standards
- Currents > 2 μA
- Current dependence
- Underestimation of error
Data summary
- Mean difference:
r format(mean(carb_data$fmbc_diff), digits = 1) - Standard deviation of difference:
r format(sd(carb_data$fmbc_diff), digits = 2)
]
.right-column[ .right[
ps <- 0.3 dw <- 0.03 yl <- .1 secs <- ggplot(carb_data, aes(fm_consensus, fm_diff, color = Cur)) + geom_hline(yintercept = 0) + geom_smooth(method = "lm", se = FALSE) + geom_pointrange(aes(ymin = fm_diff - merr, ymax = fm_diff + merr), position = position_dodge2(width = dw), size = ps,) + lims(x = c(-0.05, 1.1), y = c(-yl, yl)) + labs(title = "Difference from expected Fm", subtitle = "Uncorrected", colour = "Cur (μA)", y = "Fm difference") + theme_classic() + theme_xaringan(title_font_size = 18, text_font_size = 16, background_color = "#ffffff") + theme(axis.title.x = element_blank(), axis.text.x = element_blank(), legend.direction = "horizontal", legend.position = c(.80, 1), legend.justification = "bottom") secs_lbc <- ggplot(carb_data, aes(fm_consensus, fmbc_diff, color = Cur)) + geom_hline(yintercept = 0) + geom_smooth(method = "lm", se = FALSE) + geom_pointrange(aes(ymin = fmbc_diff - merr, ymax = fmbc_diff + merr), position = position_dodge2(width = dw), size = ps) + lims(x = c(-0.05, 1.1), y = c(-yl, yl)) + labs(subtitle = "Blank corrected", x = "Fm expected", y = "Fm difference") + theme_classic() + theme_xaringan(title_font_size = 18, text_font_size = 16, background_color = "#ffffff") + theme(legend.position = "None") secs / secs_lbc
] ]
???
precision, accuracy issues with blank and normalization
MICADAS
.pull-left[
Coming soon(ish)...
Source differences
- One target in source at a time - lower blank
- Gas supplied via conical joint in side of target
Inlet differences
- MICADAS uses closed inlet
- CO2 from carbonates transferred onto resin trap in stream of helium
- Sample introduced by adding helium and adjusting flow using variable volume ]
.pull-right[
.right[MICADAS Gas Interface]
]
???
More pronounced current vs. flow curve makes measurement more difficult at variable CO2 flow. Salazar and Szidat are developing an open split interface with EA - squish peak with dead volume
photo: micadas source
Summary
NOSAMS has developed a successful method for analyzing radiocarbon in carbonates with an open-split interface delivering CO2 gas to a NEC MCSNICS gas-accepting cesium sputter ion source.
Goals:
- Finish characterization of the method
- Compare to graphite with test samples
- Improve accuracy
- Better understand method blank
- Reduce sample size
- Automate data acquisition and reduction
Thank You!
Mark Roberts, Josh Burton, Josh Hlavenka, Al Gagnon, Kalina Gospodinova, Mark Kurz, and the staff of NOSAMS
Karl von Reden
Cam McIntyre, SUERC
Ted Ognibene, CAMS
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