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Heavy Ions

Q: What are some common challenges in working with heavy ions?

Working with heavy ions can present some unique challenges, such as ensuring proper shielding and safety protocols, managing the high energy levels, and optimizing the experimental conditions to achieve reliable and reproducible results. Researchers must also carefully consider the specific type of heavy ion and its interactions with the target material or tissue.

Heavy ions refer to atomic nuclei heavier than hydrogen, such as carbon, oxygen, or iron.
These high-energy particles have unique properties and interactions that make them valuable for a range of scientific and medical applications, including cancer therapy, space radiation studies, and fundamental physics research.
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Most cited protocols related to «Heavy Ions»

Automated Peptide Quantification Suite

Instrument-specific RAW files were converted to the mzXML file format27 (link) using ReAdW.exe (http://sashimi.sourceforge.net) and imported into a relational database using MySQL (http://www.mysql.com). Data analysis was performed using the in-house GFY analysis suite.
Peptide quantification and S/N determination were performed using Vista, an automated software suite developed in-house. The software functioned as follows: the theoretical masses of both labeled and unlabeled species were determined from the sequence composition of each peptide. These masses were then used to extract ion chromatogram intensities separately for each species from high resolution MS spectra within 20 MS survey scans and within a user-defined mass window. The mass window was separately defined for each run at ±5σ of the total mass accuracy distribution of all estimated confidently assigned peptides. Spectral peaks for each species were separated from surrounding noise using an iterative mass precision algorithm described in the text. Chromatographic peak boundaries were determined by extending each peak from the location of the data-dependent MS/MS scan within which the peptide was identified to a dynamically determined noise baseline, incorporating regions where the MP z-score was greater than zero. This noise baseline was calculated from all peaks observed within a ±25 m/z window around the theoretical species masses and within the adjacent ±20 MS spectra. The S/N ratio for each species was determined as the ratio of the maximum chromatographic peak intensity observed to the noise baseline, as calculated in the text. The area under the curve was then separately determined for each species, and compared to generate a relative abundance ratio. The monoisotopic peak for each species only was used to generate the area measurements; we note that, while multiple peaks may be used in theory, we found that such an approach is problematic at low signal levels with extreme quantitative ratios due to issues with the limit of detection of the mass spectrometer.
Quantified peptide species were individually scored for quality using both the Random Forest classifier16 and a heuristic score. This score represents a weighted average of a variety of empirically determined Boolean predictors, including signal-to-noise ratios, number of observations across the chromatographic peak, mass accuracy statistics, unlabeled/labeled pair coelution, distance from the tandem MS scan, split peak signature, and encapsulation of the surrounding data. Except where otherwise noted, all successfully quantified peptides within 5σ of the mean log₂ ratio were included in all analyses, regardless of score. Vista also includes additional enhancements to improve quantification, including rescaling of the heavy peptide ion series, deconvolution to normalize for label impurity, and compensation for the interconversion of arginine to proline, as is sometimes seen in studies using SILAC.29 (link)
The software supports data supplied in the pepXML and mzXML formats,27 (link) from a wide variety of high-mass-accuracy-capable instrumentation, analyzed using any of a variety of search algorithms. The algorithm also supports most differential labeling methods, including SILAC,6 (link) cleavable ICAT,30 (link) and CAR.24 (link) It is further extensible to deuterium-based approaches.
All analyses in this study were performed on a dual-processor, dual-core 3.00 GHz Xeon system running Fedora Core 7. On average, Vista currently performs approximately one analysis per second; a typical analysis of 3445 peptides from the 1:1 labeled sample using LTQ FT data took 57 min.
More information on the Vista software suite, including information on obtaining the software, can be found at http://vista.hms.harvard.edu/.

Bakalarski C.E., Elias J.E., Villén J., Haas W., Gerber S.A., Everley P.A, & Gygi S.P. (2008). The Impact of Peptide Abundance and Dynamic Range on Stable-Isotope-Based Quantitative Proteomic Analyses. Journal of proteome research, 7(11), 4756-4765.

Publication 2008

Arginine Chromatography Deuterium Heavy Ions Peptides Proline Radionuclide Imaging Tandem Mass Spectrometry Vision

Comprehensive Validation of gPMC Code

A set of studies was performed to demonstrate the functionality of the new gPMC code, as well as to comprehensively validate its accuracy. Specifically, total dose, dose of primary protons, secondary protons and other heavier ions, fluence of primary protons and secondary protons, as well as LETd were compared to the results computed by TOPAS (Perl et al 2012 (link)). For dose comparison, pencil beam with zero width was simulated and the result was integrated laterally to obtain the dose distribution of a broad beam with a size of 5×5 cm². The choice of a pencil beam was because dose and fluence distributions on the central beam axis from this infinitesimal beam are very sensitive to discrepancies in angular deflection and angular distribution of protons. In contrast, the broad beam is more realistic and was hence used to evaluate the accuracy in a more clinically relevant setup. For LETd comparison, 2×2 cm² broad beams were studied. The phantom we used was a pure water phantom of 10.1×10.1×30 cm³ in dimension with a voxel size of 0.1×0.1×0.1 cm³. 100 MeV and 200 MeV mono-energetic beams normally impinged on the phantom surface.
The second scenario studied was a prostate cancer patient. gPMC v1.0 was reported to have a systematic overestimation in dose at the entrance region and underestimation at the target for prostate cases due to approximations in nuclear interaction models (Giantsoudi et al., 2015 (link)). To demonstrate the improvements made in this new version, a prostate cancer patient with two laterally opposite beams was used. Dose in this patient was computed with gPMC v1.0, gPMC v2.0 and TOPAS.
The efficiency and cross-platform portability of gPMC v2.0 were tested with several different devices including an NVidia GeForce GTX TITAN GPU card, an AMD Radeon R9 290x GPU card, an Intel i7-3770 CPU processor and an Intel Xeon E5-2640 CPU processor. We also conducted tests with different numbers of dose counters to investigate its impact on the memory conflict issue.

Qin N., Botas P., Giantsoudi D., Schuemann J., Tian Z., Jiang S.B., Paganetti H, & Jia X. (2016). Recent developments and comprehensive evaluations of a GPU-based Monte Carlo package for proton therapy. Physics in medicine and biology, 61(20), 7347-7362.

Publication 2016

Epistropheus Heavy Ions Medical Devices Memory Patients Prostate Prostate Cancer Protons

Comparative Radiosensitivity of CHO Cells

Exponentially growing cells were exposed to radiation at room temperature. Gamma-ray irradiations (LET 0.3 keV/µm) were carried out at a dose rate of 2.5 Gy/min using a Model Mark I-68A nominal 222TBq (6,000 Ci) ¹³⁷Cesium sealed source (J.L. Shepherd, Carlsbad, CA). Hadron radiation experiments were carried out at the National Institute of Radiological Sciences (NIRS) in Chiba, Japan [19 (link)]. Protons were accelerated to 70 MeV (LET 1.1 keV/µm) using the NIRS-930 cyclotron at NIRS [20 (link)]. Carbon-ions and iron-ions were accelerated to 290 MeV/n and 500 MeV/n, respectively, using the heavy ion medical accelerator (HIMAC) at NIRS. The LET of the entrance region for monoenergetic carbon ions and monoenergetic iron ions were 13 and 200 keV/μm, respectively. Monoenergetic carbon ions with a LET of 70 keV/μm were obtained by Lucite attenuation. The dose-averaged LET of the carbon ions at the middle of the 6-cm spread-out Bragg peak (SOBP) is ∼50 keV/µm at a distance of 119 mm from the entrance [21 (link)]. Dose rates for carbon-ions, iron-ions and protons were set at 3 Gy/min. All irradiation was carried out at room temperature. LET values of the various radiation types are summarized in Table 1.

Table 1.

Ratios of D₁₀ doses for CHO wild-type and xrs5 cells exposed to various radiations

Radiation	LET keV/μm	(A) D₁₀ of CHO10B2	(B) D₁₀ of xrs5	Ratio (A)/(B)
Gamma rays	0.3	6.37 Gy	1.18 Gy	5.40
Proton Mono	1.1	5.31Gy	1.16 Gy	4.58
Carbon Mono	13	3.79 Gy	0.91 Gy	4.16
Carbon SOBP	50	3.16 Gy	1.05 Gy	3.01
Carbon Mono	70	2.49 Gy	0.94 Gy	2.65
Iron Mono	200	1.89 Gy	1.00 Gy	1.89
⁶⁴Cu-ATSM	NA	0.97 Bq/cell	0.40 Bq/cell	2.43

McMillan D.D., Maeda J., Bell J.J., Genet M.D., Phoonswadi G., Mann K.A., Kraft S.L., Kitamura H., Fujimori A., Yoshii Y., Furukawa T., Fujibayashi Y, & Kato T.A. (2015). Validation of 64Cu-ATSM damaging DNA via high-LET Auger electron emission. Journal of Radiation Research, 56(5), 784-791.

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Publication 2015

Carbon Cells Cyclotrons Gamma Rays Heavy Ions Ions Iron Lucite Protons Radiation Radiation Exposure Radiotherapy X-Rays, Diagnostic

Cellular Radiation and Chemical Response

Cells were irradiated with TITAN x-ray irradiator with 200 kVp, 20 mA, 0.5 cm of Al and Cu filter (Shimadzu, Japan). Heavy ion treatment was performed by HIMAC (Heavy Ion Medical Accelerator in Chiba). The accelerated ions used in this study were carbon ions (290 MeV/n), neon (400 MeV/n), silicon (490 MeV/n), argon (500 MeV/n), and iron ions (500 MeV/n). The details concerning the beam characteristics of carbon-ion beams, biological irradiation procedures, and dosimetry have been described elsewhere [19 (link),20 (link)]. We used several kinds of beams having different LET values, using Lucite absorbers with various thicknesses to change the energy of the beams. At the sample position, we estimated the LET values of carbon (13, 30, 50, 70 keV/μm), neon (31, 70, 120 keV/μm), silicon (55, 150, 250 keV/μm), argon (100 keV/μm), and iron (200 keV/μm). Taking fragmentations into consideration, dose was calculated from fluence [21 (link)-23 ]. Asynchronously dividing cells cultured in T12.5 flasks were irradiated at room temperature. For chemical treatment, cycling cells in T12.5 culture flasks were exposed to series of concentration of bleocin, a single component of bleomycin family group A (Calbiochem, Japan), which induces DNA strand breaks, camptothecin (CPT, Sigma, Japan) which is a Topoisomerase I inhibitor, mitomycin C (MMC, Funakoshi, Japan) which induces DNA crosslink, or cisplatin (Nippon Kayaku, Japan) which induces DNA crosslink for 1 hour at 37°C.
After exposure to ionizing radiation or chemical treatment, cells were trypsinized and re-plated in P-100 cell culture dishes. HeLa and U87-MG cells were cultured for 10 to 14 days, and U-CH1-N cells were kept in an incubator for 3 to 4 weeks. Plating efficiency of U-CH1-N, U87-MG, and HeLa cells were 4.8%, 32%, and 70%, respectively. After colonies were formed, cells were fixed with 100% ethanol and stained with crystal violet solution. Colonies were observed under microscope and colonies containing more than 50 cells were counted as survivors. Cell survival assay was carried out 2 to 4 times independently. Radiation exposed cell survival curves were fitted with linear quadratic model by PRISM5 software on MacOSX10.6. Error bars indicate standard error of the means.

Kato T.A., Tsuda A., Uesaka M., Fujimori A., Kamada T., Tsujii H, & Okayasu R. (2011). In vitro characterization of cells derived from chordoma cell line U-CH1 following treatment with X-rays, heavy ions and chemotherapeutic drugs. Radiation Oncology (London, England), 6, 116.

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Publication 2011

Argon Biological Assay Biopharmaceuticals Bleomycin Camptothecin Carbon Carbon-13 Cell Culture Techniques Cells Cell Survival Cisplatin DNA Breaks Electromagnetic Radiation Ethanol Heavy Ions HeLa Cells Hyperostosis, Diffuse Idiopathic Skeletal Iron Lucite Microscopy Neon Radiation Exposure Radiometry Silicon Survivors Topoisomerase I Inhibitors Violet, Gentian X-Rays, Diagnostic

Automated Deuterated Peptide Quantification

The peptide library workflow (Fig.1A) - The initial DDA peptide library consists of peptides containing leucines within first/last five residues [7 (link)]. The library is refined by ranking the peptides based on their monoisotopic peak XIC intensities. The top-ranked peptides are then reanalyzed using PRM (R=17.5K) to classify peptides considering the quality and intensities of their leucine-containing fragment ions. The final candidate peptide library comprises the inclusion list for PRM at the higher resolutions settings (e.g. R=140K) in order to detect D3-Leu tracer.
Detection and quantification of light and heavy target ions (Fig. 1B) - XPI is an automated pipeline for, but not limited to, PRM-dependent D3-Leu tracer enrichment in clinically derived HDL apoA-I. From the peptide library, XPI generates all the possible leucine-containing fragment ions including light (M0) and heavy (2HM3) by considering the mass shift (3.01883025 Da) induced by D3-Leu labeling. XPI extracts data from mzML files and carries out pre-processing steps including intensity centroiding, background subtraction, and noise removal using Pymzml. With the collected data, XPI finds target fragment ions by matching the scan number, the Δmass window (=|theoretical mass – observed mass|) and the RT range. In order to accommodate the RT shift incurred by the deuterium, and to identify the low abundant 2HM3 peak efficiently, XPI performs RT refinement steps by reference to M0 ion detection since the M0 peaks are readily identified. This extra refinement step excludes non-specific peaks that appear in a larger RT window, and uses the LOWESS algorithm, the local maxima and minima method, to do so. XPI provides nine methods for the quantification: the sum, max, top3, median, average, qsum, qtop3, qmax and qaverage (Fig. 1B). The sum, max, top3, median and average options calculate a representative value from the whole set of collected PRM intensities, while the others, qsum, qtop3, qmax and qaverage, calculate the intensity from the second and third quartiles. XPI finally calculates ratios between M0 and 2HM3 ions in two ways: 1) heavy/light or light/heavy, or 2) enrichment (Heavy/(Heavy+Light)).
Validation and visualization (Fig. 1C) - Since XPI has nine options for quantification, XPI shows which method fits best to a given dataset by drawing box-plots based on the R² between the intended and observed ratio (Fig. 1C). When possible, we recommend using standard mixtures to evaluate the best quantification method. The user can choose the best quantification method by comparing the average or median R² of the standard data. XPI provides a standard curve plot with a regression line to check the linearity of the standard data (Fig. 1C). XPI also provides the summed M0 ion intensity versus accumulated ratio plots for filtering/excluding low and outlier enrichment PRM ions (Supplemental Information). XPI draws various plots at the protein and peptide level. Two- and three-dimensional mass profiles with a color scheme using the Δmass are supplied for visualization and validation of the target ion identification if needed. Since XPI uses Python scripts, it is executable regardless of the operating system as long as Python 3 and the required packages are installed. XPI can process 7 mzML files (~1.6 gigabytes) within 12 minutes with Intel Core i7 (2.6 GHz), 16Gb memory and OS X 10.11.2 environment. XPI is downloadable at http://cics.bwh.harvard.edu/software.

Lee L.H., Andraski A.B., Pieper B., Higashi H., Sacks F.M., Aikawa M, & Singh S.A. (2017). Automation of PRM-dependent D3-Leu tracer enrichment in HDL to study the metabolism of apoA-I, LCAT and other apolipoproteins. Proteomics, 17(1-2), 10.1002/pmic.201600085.

Publication 2017

Apolipoprotein A-I cDNA Library CIC protein, human Deuterium Heavy Ions Light Memory Peptide Library Peptides Proteins Python Radionuclide Imaging Seizures Topoisomerase, TOP3

Most recents protocols related to «Heavy Ions»

Fabrication of Quasi-BCC Nanolattices

The gold and copper quasi-BCC nanolattices were prepared by electrochemical deposition in the channels of heavy ion track templates. First, the polycarbonate (PC) foils were irradiated by swift heavy ions at the Heavy Ion Research Facility at Lanzhou (HIRFL) with 9.5 MeV per nucleon ²⁰⁹Bi ions. The thickness of the templates was 30 µm and the fluence of irradiation was 7.1 × 10⁸ or 7.1 × 10⁹ cm⁻² in four directions. Following that, each side of the template was illuminated with UV light for 2 h. The purpose of this step was to make the track etching rate of the template much larger than the bulk etching rate during the etching process, to ensure the uniform channel after etched. Then, the template was placed in 50 °C, 5 M NaOH solution and etched for a certain time to obtain the template with a certain aperture channel. After that, the etched template was rinsed several times in deionized water immediately and then immersed in deionized water for 5 minutes to remove the remaining etchant from the template to avoid over-etching. A thin layer of gold was sputtered on one template side as an electrochemically deposited cathode and a layer of copper was deposited on the same side to increase the strength of the template using electrolyte consisting of 75 g L⁻¹ CuSO₄⋅5H₂O and 30 g L⁻¹ H₂SO₄. The electrolyte used for gold and copper quasi-BCC nanolattices deposition on the other side were 75 g L⁻¹ Na₃Au(SO₃)₂ or 75 g L⁻¹ CuSO₄⋅5H₂O and 30 g L⁻¹ H₂SO₄ solution. Last, the PC templates with quasi-BCC nanolattices was placed in dichloromethane (CH₂Cl₂) solution to dissolve organic components to obtain gold and copper quasi-BCC nanolattices. All the quasi-BCC nanolattices were stored in ethanol.
The PC template obtained by chemical etching and the electrochemically deposited quasi-BCC nanolattice structure were complementary structures. By weighing out the PC template before and after chemical etching, the relative density of the prepared gold and copper quasi-BCC nanolattice was given by formula (2)^{38 (link)}:

\bar{ρ} = \frac{V - V_{e}}{V} = \frac{M - M_{e}}{M}

where V is the overall volume of the template before etching; V_e is the volume of the template after etching; M is the overall mass of the template before etching; M_e is the mass of the template after etching.

Cheng H., Zhu X., Cheng X., Cai P., Liu J., Yao H., Zhang L, & Duan J. (2023). Mechanical metamaterials made of freestanding quasi-BCC nanolattices of gold and copper with ultra-high energy absorption capacity. Nature Communications, 14, 1243.

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Publication 2023

Bismuth-209 Copper Dietary Fiber Electrolytes Ethanol Gold Heavy Ions Ion Channel Ions Methylene Chloride polycarbonate Radiotherapy Ultraviolet Rays

Ion Beam Milling of Vitrified Samples

The twenty crystals were divided into four groups of five, one group for each plasma ion-beam to test. For each gas source, crystals were milled sequentially using identical milling strategies—the same preset pattern, beam currents, and times. The Z-depth of the patterns and currents were the only parameters adjusted. For each crystal and gas source, the milling was conducted in four steps with approximately the same parameters summarized in Supplementary Table 2. Generally, all milling was conducted using the cleaning cross section for each pattern with 85% overlap between both X and Y spot positions. The first milling step used an ~1 nA current to mill two boxes of 6 × 6 μm separated in the middle by 2 μm. The second utilized two cleaning cross sections of 5 × 1 µm in size separated by 1μm that used an ~0.3 nA current. The third step consisted of two 5 × 0.5 µm boxes separated by 0.5 μm with a beam current of 0.1 nA. The final milling step consisted of two 5 × 0.3 µm boxes separated by 300 nm that were milled with a beam current of ~30 pA. Currents between sources were chosen to be within 1 aperture number from the prior source to minimize the number of alignments between experiments (Supplementary Table 2). All cleaning cross sections above the lamella were milled from the top to the bottom, whereas all the cross sections below the lamella were milled from the bottom to the top. The sputtering rate for a drawn pattern in the microscope software is set to solid silicon, which is much denser than vitrified water or biological materials. We empirically determined reasonably adapted milling times by varying the dictated Z dimension, or depth of the drawn patterns. For xenon, we used a depth setting of 5, 3, 2, and 2 μm deep for each step. For argon, these were 6, 3, 2, 2 μm deep. For nitrogen, we used 20, 10, 4, 4 μm. Finally, we used 8, 6, 4, 4 μm for the oxygen beam. These settings are summarized in Supplementary Table 1. In most cases, these were higher than strictly necessary to ensure second passes would not be needed. However, even with the depths used, the nitrogen lamellae required constant manual intervention that was still unable to rescue some of the lamellae. Typically, argon and xenon lamellae were completed with total milling times of between 4 and 20 mins depending on alignments between milling steps and various manual microscope operations. Each nitrogen lamella took ~15−30 mins of on-sample milling time. For oxygen, this was similarly 15−30 mins per lamella. A complication to the timing was the manual operation and shortcomings of specific gasses. For example, focusing the argon and xenon beams is more challenging than a gallium beam, but straightforward. The oxygen and nitrogen beams are very difficult to focus and align at low beam currents. Positioning lamellae was also much easier for the heavier ions since the focused images were much sharper in general. Finally, imaging lamellae using the various ion beams changes the contrast in the electron beam due to the differential breakdown of the GIS-deposited platinum over time and differing by each ion. For example, oxygen lamella #2 (Supplementary Fig. 8) was all but invisible after milling, and even after repeated attempts, the SEM image had to be zoomed out to locate the lamella. In our experiments, the contrast changing of the GIS deposited platinum without the ion-assisted deposition described herein was much worse, essentially making many attempts at milling with nitrogen or oxygen much more challenging than simply using a gallium beam source.

Martynowycz M.W., Shiriaeva A., Clabbers M.T., Nicolas W.J., Weaver S.J., Hattne J, & Gonen T. (2023). A robust approach for MicroED sample preparation of lipidic cubic phase embedded membrane protein crystals. Nature Communications, 14, 1086.

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Publication 2023

Argon Biopharmaceuticals Catabolism Electrons Gallium Heavy Ions Microscopy Nitrogen Oxygen Plasma Platinum Silicon Xenon

Heavy Metal Stress on E. coli Growth

E. coli (K12 MG1655 strain) is cultured in defined media M63 to achieve an OD of 0.5 and supplemented with 1% (w/v) glucose to mitigate conflating stress from heavy metal stress ions with nutrition limitation. The subcultures prepared as described in the prior section were washed with 1 mL PBS twice and resuspended in M63 defined media. M63 media supplemented with 1% glucose (w/v) was pipetted into wells of white-opaque 96-well microplates. Different concentrations of heavy metal (NaAsO₂ or K₂Cr₂O₇) were added to the wells. Specifically, 0, 1, 10, 100, and 1,000 µM of NaAsO₂ and 0, 0.34, 3.4, 34, and 170 µM of K₂Cr₂O₇ were exposed to cultures for 2 h. The resuspended culture was pipetted into the wells to make the OD of the culture 0.5. Each condition was done in biological duplicates. After pipetting, the microplate was placed in the SkanIt Microplate Reader (Thermo Scientific) at 37 °C and shaken at a speed of 300 rpm and high force. The OD of the culture in each well was measured every 5 min for 6 h to generate growth curves.
Preparation of cultures exposed to tap water and wastewater from Orange County Sanitation District (OCSD) involves similar steps as those exposed to DI water spiked with As³⁺, except after washing with PBS, the subculture was resuspended in tap water or wastewater supplemented with 1% (w/v) glucose at an OD of 0.5, and the heavy metal salts were dissolved in tap water or wastewater instead of the defined media. The secondary treated wastewater was treated by primary sedimentation followed by an activated sludge process with nitrification and denitrification at OCSD. Before spiking with As³⁺, the secondary treated wastewater was filtered with 0.45 µm MCE Membrane (MF-Millipore).

Wei H., Huang Y., Santiago P.J., Labachyan K.E., Ronaghi S., Banda Magana M.P., Huang Y.H., C. Jiang S., Hochbaum A.I, & Ragan R. (2023). Decoding the metabolic response of Escherichia coli for sensing trace heavy metals in water. Proceedings of the National Academy of Sciences of the United States of America, 120(7), e2210061120.

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Publication 2023

Biopharmaceuticals Culture Media Denitrification Escherichia coli Glucose Heavy Ions Ions Metals Metals, Heavy Nitrification Potassium Dichromate Salts Sludge Tissue, Membrane

SERS-Based Heavy Metal Sensing Protocol

Spectral data of lysate samples are acquired by placing a droplet with a volume of 25 µL of lysate from E. coli cells untreated (control) or exposed to heavy metal ion solutions at various concentrations on SERS surfaces. The measured concentration range for NaAsO₂ was 0.65 pg/L to 650 mg/L (13 concentrations) and for K₂Cr₂O₇ was 0.1 ng/L to 10 mg/L (nine concentrations) spaced by one order of magnitude as shown in Table 1. The corresponding concentrations in molarity of As³⁺ and Cr⁶⁺ are shown in Table 1.
For each exposure concentration, a dataset of 1,200 SERS spectra is acquired using a Renishaw InVia™ micro Raman system with an integration time of 0.5 s, 146 µW laser power at 785 nm excitation wavelength, and a 60× water immersion lens with 1.2 NA (beam diameter of 292 nm). Raman maps were acquired in an array of 20 × 20 with 3 µm steps between measurement points, resulting in 400 spectra per map. Three maps were acquired over different regions of the sample surface resulting in a total of 1,200 spectra per concentration for each metal ion defining a class for initial training of machine learning algorithms (61 (link)). The dataset acquisition takes 10 min, and the droplet does not evaporate during this period of time. In order to ensure that the algorithm is not being trained to detect batch-to-batch variations of SERS surfaces, concentration classes between two and six, including control samples, were acquired on different regions of the same SERS surface (droplets exposed to isolated regions), indicated by superscripts in Table 1. Furthermore, the control group, prepared under the same conditions in the absence of Cr⁶⁺ or As³⁺ exposure, was measured from lysate samples prepared in biological duplicates on different days, from the eight different SERS surfaces, also fabricated on different days, used for the other metal concentrations exposure conditions to train algorithms to not identify differences based on normal variability of experimental conditions such as culture growth, device fabrication, and processing steps.

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Publication 2023

Biopharmaceuticals Cells Escherichia coli Heavy Ions Lens, Crystalline Medical Devices Metals Metals, Heavy Microtubule-Associated Proteins Potassium Dichromate Submersion

Bt Toxin Persistence in Heavy Metal Contaminated Soil

Fifty grams of black soil was added to a polyethylene plastic bag and mixed with a certain concentration of heavy metal ions. The concentration of Bt toxin in the black soil was 5.0 µg/g, and the concentration of Pb(II) or Zn(II) in the black soil ranged from 0 to 800 mg/kg. The maximum water-holding capacity was adjusted to 60% with distilled water. The soil samples were incubated in a climate chamber at 25 ± 1 °C for 0, 2, 5, 10, 18, 28 and 60 days. At each time interval, soil samples were taken out to measure the Bt toxin concentration by ELISA [36 ,37 ].

Wang Y., Zhou X., Zhang F., Zhang L., Yang P, & Maimaitiniyazi R. (2023). Effects of Pb(II) and Zn(II) Contamination on Adsorption, Desorption and Degradation of Cry1Ac Toxin Identical to Bt Transgenic Poplar in Black Soil. Toxics, 11(2), 89.

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Publication 2023

Bacillus thuringiensis Toxins Climate Enzyme-Linked Immunosorbent Assay Heavy Ions Ions Metals Metals, Heavy Polyethylene, High-Density Vigna mungo

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What are heavy ions and how are they used in scientific research?

What are some common challenges in working with heavy ions?

How can PubCompare.ai help with heavy ions research?

PubCompare.ai is an AI-driven platform that can greatly enhance heavy ions research by helping researchers more efficiently screen protocol literature and leverage AI to pinpoint critical insights. The platform's AI-driven analysis can highlight key differences in protocol effectiveness, enabling researchers to choose the best option for reproducibility and accuracy in their heavy ions experiments. This can help streamline the research process and lead to more reliable results.

What are some of the different types or variations of heavy ions used in research?

There are several different types of heavy ions used in research, including carbon, oxygen, iron, and other heavier elements. Each type of heavy ion has its own unique properties and interacttions, and researchers must carefully select the appropriate ion based on their specific research goals and experimental requirements. For example, carbon ions are often used in cancer therapy due to their ability to precisely target and damage tumor cells.

Can you provide some examples of how heavy ions are applied in different fields of study?

Heavy ions have a wide range of applications across various fields of study. In medicine, they are used in cancer therapy, where the precise targeting and damage caused by heavy ions can be more effective than traditional radiation treatments. In space research, heavy ions are studied for their role in cosmic radiation and its effects on astronauts and spacecraft. Additionally, heavy ion research is important in fundamental physics, where researchers use these particles to study the properties of matter and the interactions of subatomic particles.

More about "Heavy Ions"

Atomic nuclei heavier than hydrogen, such as carbon, oxygen, or iron, are referred to as heavy ions.
These high-energy charged particles exhibit unique properties and interactions, making them invaluable for a variety of scientific and medical applications, including cancer therapy, space radiation studies, and fundamental physics research.
The power of PubCompare.ai, an AI-driven platform, can significantly enhance heavy ions research.
By effortlessly locating the most reproducible protocols from literature, preprints, and patents, researchers can leverage AI-powered comparisons to identify the optimal protocols and products for their heavy ions experiments.
This streamlines the research process and helps achieve reliable, reproducible results.
In the context of heavy ions research, related terms and concepts include ionizing radiation, charged particle beams, linear energy transfer (LET), Bragg peak, and hadron therapy.
Abbreviations like HI (heavy ions) and LHI (light heavy ions) are also commonly used.
Specialized equipment and materials like the Optima 8000 mass spectrometer, Kapton film, and TITAN-320 particle accelerator are often employed in heavy ions research.
Additionally, cell culture media like RPMI 1640 and fetal bovine serum (FBS) are used to study the biological effects of heavy ions.
Advanced analytical techniques, such as scanning electron microscopy (S-4800) and the ASAP 2020 surface area and porosity analyzer, can provide valuable insights into the physical and chemical properties of heavy ions and their interactions.
By incorporating these synonyms, related terms, abbreviations, and key subtopics, researchers can optimize their content for search engines and provide a comprehensive, informative, and easy-to-read resource on the topic of heavy ions research.