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Indium

Indium is a soft, silvery-white metallic element with the atomic number 49.
It is an essential trace mineral that plays a role in various biological processes, including enzyme activation and immune function.
Indium is commonly used in the production of electronic devices, such as liquid crystal displays (LCDs) and solar cells.
Research on the applications and health effects of indium is an area of ongoing scientific study.
PubCompare.ai can help optimize your indimum research by providing AI-driven comparisons to locate the most reliable protocols from scientific literature, pre-prints, and patents, enhancing reproducibility and accuracy to ensure you find the best methods and products for your indium-related projects.

Most cited protocols related to «Indium»

1
Clustering Dynamic Functional Connectivity

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Publication 2014
Indium Joints, Elbow
2
Estimating SNP Heritability in Autism
SNP heritability, hG2 , was estimated using LDSC19 (link) for the full ASD GWAS sample and GCTA84 (link),116 ,117 for subsamples too small for LDSC. For LDSC we used precomputed LD scores based on the European ancestry samples of the 1000 Genomes Project118 restricted to HapMap3119 SNPs. The summary stats with standard LDSC filtering were regressed onto these scores. For liability scale estimates, we used a population prevalence for Denmark of 1.22%18 (link). Lacking proper prevalence estimates for subtypes, we scaled the full spectrum prevalence based on the composition of the case sample.
For subsamples too small for LDSC, the GREML approach of GCTA84 (link),116 ,117 was used. On best guess genotypes (genotype probability > 0.8, missing rate < 0.01 and MAF > 0.05) with INDELs removed, a genetic relatedness matrix (GRM) was fitted for the association sample (i.e. the subjects of European ancestry with π^≤0.2 ) providing a relatedness estimate for all pairwise combinations of individuals. Estimation of the phenotypic variance explained by the SNPs (REML) was performed including PC 1–4 as continuous covariates together with any other PC that was nominally significantly associated to the phenotype as well as batches as categorical indicator covariates. Testing equal heritability for non-overlapping groups was done by permutation test (with 1000 permutations) keeping the controls and randomly assigning the different case labels.
Following Finucane et al.87 (link), we conducted an enrichment analysis of the heritability for SNPs for functional annotation and for SNPs located in cell-type-specific regulatory elements. Using first the same 24 overlapping functional annotations (stripped down from 53) as in Finucane et al. we regressed the χ2 from the ASD GWAS summary statistics on to the cell-type specific LD scores download from the site mentioned above with baseline scores, regression weights and allele frequencies based on European ancestry 1000 Genome Project data. The enrichment of a category was defined as the proportion of SNP heritability in the category divided by the proportion of SNPs in that category. Still following Finucane et al. we did a similar analysis using 220 cell type–specific annotations divided into 10 overlapping groups. In addition to this, we conducted an analysis based on annotation derived from data on H3K4Me1 imputed gapped peaks data from the Roadmap Epigenomics Mapping Consortium120 ; more specifically information excluding the broad MHC-region (chr6:25–35MB).
Grove J., Ripke S., Als T.D., Mattheisen M., Walters R.K., Won H., Pallesen J., Agerbo E., Andreassen O.A., Anney R., Awashti S., Belliveau R., Bettella F., Buxbaum J.D., Bybjerg-Grauholm J., Bækvad-Hansen M., Cerrato F., Chambert K., Christensen J.H., Churchhouse C., Dellenvall K., Demontis D., De Rubeis S., Devlin B., Djurovic S., Dumont A.L., Goldstein J.I., Hansen C.S., Hauberg M.E., Hollegaard M.V., Hope S., Howrigan D.P., Huang H., Hultman C.M., Klei L., Maller J., Martin J., Martin A.R., Moran J.L., Nyegaard M., Nærland T., Palmer D.S., Palotie A., Pedersen C.B., Pedersen M.G., Poterba T., Poulsen J.B., St Pourcain B., Qvist P., Rehnström K., Reichenberg A., Reichert J., Robinson E.B., Roeder K., Roussos P., Saemundsen E., Sandin S., Satterstrom F.K., Smith G.D., Stefansson H., Steinberg S., Stevens C.R., Sullivan P.F., Turley P., Walters G.B., Xu X., Stefansson K., Geschwind D.H., Nordentoft M., Hougaard D.M., Werge T., Mors O., Mortensen P.B., Neale B.M., Daly M.J, & Børglum A.D. (2019). Identification of common genetic risk variants for autism spectrum disorder. Nature genetics, 51(3), 431-444.
Publication 2019
Cells Europeans Genome Genome-Wide Association Study Genotype INDEL Mutation Indium Phenotype Single Nucleotide Polymorphism Strains
3
Socio-demographic Index: Measuring Societal Development

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Publication 2017
Age Groups Birth Head Human Development Indium Population Group
4
Optimizing Just-in-Time Adaptive Interventions: Experimental Approaches
There are three main approaches that can be currently used to optimize JITAIs. As Collins et al. (2005) (link) note, behavioral interventions have traditionally been optimized using a series of randomized controlled trials (RCTs). As an experimental method, RCTs are designed to assess whether, on average, the intervention package as a whole had an effect on the behavior of interest. However, RCTs are not designed to investigate which components of an intervention are efficacious, when they are efficacious, or what psychosocial or contextual factors influenced their efficacy. In secondary analyses of RCT data, random assignment facilitates the assessment of treatment-effect moderation with respect to baseline characteristics (e.g., age), which provides information about static factors that influence efficacy of the intervention package (e.g., Hekler et al., 2013 (link)). However, this is insufficient for JITAI development, where we must evaluate what time-varying factors influence the efficacy of different components in order to understand when and in what contexts a particular intervention option should be delivered. If there is sufficient variability across time in receipt of a component due to non-adherence or implementation problems (e.g., participants’ medication adherence goes up and down over time), then RCT data can be used to investigate time-varying moderation and effects of time-varying components. However, as is well known, in such secondary analyses, baseline randomization offers no protection against causal confounding, and results are subject to bias. Thus, important questions pertaining to JITAI optimization—when a particular intervention component should be delivered, what factors at the time of delivery affect whether the intervention component will have the desired effect, and so on—are poorly addressed by data from standard RCTs.
As an alternative to RCTs, there has been a resurgence of interest in the use of single-case experimental designs (SCEDs) to develop and evaluate mHealth interventions (Dallery & Raiff, 2014 ; Dallery, Cassidy, & Raiff, 2013 (link)). SCEDs enable highly efficient preliminary efficacy testing of an intervention component, since each participant acts as his or her own control. However, in their traditional forms (i.e., reversal, multiple-baseline, and changing-criterion designs), SCEDs are of little help for determining the time or context in which a certain intervention option is most efficacious. This is because SCEDs often do not clearly articulate decision points for intervention-component delivery or systematically examine moderators of observed effects.
To overcome the limitations of RCTs in guiding intervention design, Collins and colleagues (Collins, Chakraborty, Murphy, & Strecher, 2009 (link); Collins et al., 2005 (link)) proposed the use of factorial experiments as a part of the Multiphase Optimization Strategy (MOST) for multi-component interventions. Traditional factorial designs can be used to assess the effects of each individual intervention component and key interactions of interest, enabling researchers to choose, based on empirical evidence, which intervention components to include in an intervention package and at what dose. However, traditional factorial designs do not allow the determination of times when it is most effective to deliver each intervention option. Nor do these designs allow researchers to investigate what time-varying factors moderate the relative effect of different time-varying intervention components. These are key questions for JITAI development. Micro-randomized trials overcome these limitations of traditional factorial designs, and for JITAI development, they can be incorporated into MOST as an alternative experimental design in the early stages of intervention development.
Klasnja P., Hekler E.B., Shiffman S., Boruvka A., Almirall D., Tewari A, & Murphy S.A. (2015). Micro-Randomized Trials: An Experimental Design for Developing Just-in-Time Adaptive Interventions. Health psychology : official journal of the Division of Health Psychology, American Psychological Association, 34(0), 1220-1228.
Publication 2015
Behavior Therapy Early Intervention (Education) Indium Mobile Health Obstetric Delivery SERPINA3 protein, human
5
Field Testing of Revised Health Literacy Questionnaire
The revised version resulting from the focus-group feedback was field-tested in two countries (Ireland and the Netherlands). The field test included 50 computer assisted face-to-face interviews in each country conducted by JF in Ireland and VS and KS in the Netherlands. To recruit participants for this field test, judgement sampling, also known as purposeful sampling was used to guarantee an equal distribution of participants in terms of the parameters age, gender and education [25 (link)]. Due to an incorrect saving procedure data from one Dutch interview was lost, leaving a total of 99 interviews. The interview time varied from 25-90 minutes. The profile of the sample is described in Table 3.
The methodological approach concerning data analysis involved both a qualitative and quantitative analysis of the data. For the qualitative analysis, data derived from logbooks and observations made by the interviewers and general comments and feedback from participants were scrutinized using the recommendations to refine the questionnaire. The quantitative analysis involved an item analysis, Principal Component analysis (PCA) and reliability analysis on the scores of the respondents on the questionnaire items. For the item analysis, the distribution of the responses on each item was inspected to eliminate items with a low discriminative power (i.e., 95% or more of the answers in the same category). For the PCA, a separate analysis was performed for each domain (healthcare, disease prevention and health promotion), with the number of components fixed at four related to the four information-processing dimensions outlined in the health literacy matrix derived from the conceptual model and definitions and a VARIMAX rotation to yield maximum discrimination between the components. The resulting factor structures were inspected, and items without sufficient loading (< 0.30) on any of the components or with a small difference in factor loading on any two components were excluded. The remaining items were again entered into PCA. This iterative procedure was repeated until an interpretable component solution was obtained. Subscales were constructed on the basis the highest component loading of an item. The internal consistency of the scales obtained through the PCA was tested by means of the Cronbach’s alpha.
Sørensen K., Van den Broucke S., Pelikan J.M., Fullam J., Doyle G., Slonska Z., Kondilis B., Stoffels V., Osborne R.H, & Brand H. (2013). Measuring health literacy in populations: illuminating the design and development process of the European Health Literacy Survey Questionnaire (HLS-EU-Q). BMC Public Health, 13, 948.
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Publication 2013
Discrimination, Psychology Face Gender Health Literacy Health Promotion Indium Interviewers

Most recents protocols related to «Indium»

1
Indium Oxide Thin Film Deposition by ALD
Not available on PMC !

Example 1

InCl (1 eq.) was added to a Schlenk flask charged with LiCp(CH2)3NMe2 (11 mmol) in Et2O (50 mL). The reaction mixture was stirred overnight at room temperature. After filtration of the reaction mixture, the solvent was evaporated under reduced pressure to obtain a red oil. After distillation a yellow liquid final product was collected (mp˜5° C.). Various measurements were done to the final product. 1H NMR (C6D6, 400 MHz): δ 5.94 (t, 2H, Cp-H), 5.82 (t, 2H, Cp-H), 2.52 (t, 2H, N—CH2—), 2.21 (t, 2H, Cp-CH2—), 2.09 (s, 6H, N(CH3)2, 1.68 (q, 2H, C—CH2—C). Thermogravimetric (TG) measurement was carried out under the following measurement conditions: sample weight: 22.35 mg, atmosphere: N2 at 1 atm, and rate of temperature increase: 10.0° C./min. 97.2% of the compound mass had evaporated up to 250° C. (Residue <2.8%). T (50%)=208° C. Vacuum TG measurement was carried out under delivery conditions, under the following measurement conditions: sample weight: 5.46 mg, atmosphere: N2 at 20 mbar, and rate of temperature increase: 10.0° C./min. TG measurement was carried out under delivery conditions into the reactor (about 20 mbar). 50% of the sample mass is evaporated at 111° C.

Using In(Cp(CH2)3NMe2) synthesized in Example 1 as an indium precursor and H2O and O3 as reaction gases, indium oxide film may be formed on a substrate by ALD method under the following deposition conditions. First step, a cylinder filled with In(Cp(CH2)3NMe2) is heated to 90° C., bubbled with 100 sccm of N2 gas and the In(Cp(CH2)3NMe2) is introduced into a reaction chamber (pulse A). Next step, O3 generated by an ozone generator is supplied with 50 sccm of N2 gas and introduced into the reaction chamber (pulse B). Following each step, a 4 second purge step using 200 sccm of N2 as a purge gas was performed to the reaction chamber. 200 cycles were performed on a Si substrate having a substrate temperature of 150° C. in the reaction chamber at a pressure of about 1 torr. As a result, an indium oxide film will be obtained at approximately 150° C.

Example 2

Same procedure as Example 1 started from Li(CpPiPr2) was performed to synthesize In(CpPiPr2). An orange liquid was obtained. 1H NMR (C6D6, 400 MHz): δ 6.17 (t, 2H, Cp-H), 5.99 (t, 2H, Cp-H), 1.91 (sept, 2H, P—CH—), 1.20-1.00 (m, 12H, C—CH3).

Using In(CpPiPr2) synthesized in Example 2 as the indium precursor and H2O and O3 as the reaction gases, indium oxide film may be formed on a substrate by the ALD method under the following deposition conditions. First step, a cylinder filled with In(CpPiPr2) is heated to 90° C., bubbled with 100 sccm of N2 gas and the In(CpPiPr2) is introduced into a reaction chamber (pulse A). Next step, O3 generated by an ozone generator is supplied with 50 sccm of N2 gas and introduced into the reaction chamber (pulse B). Following each step, a 4 second purge step using 200 sccm of N2 as a purge gas was performed to the reaction chamber. 200 cycles were performed on the Si substrate having a substrate temperature of 150° C. in an ALD chamber at a pressure of about 1 torr. As a result, an indium oxide was obtained at 150° C.

US11859283B2. Heteroalkylcyclopentadienyl indium-containing precursors and processes of using the same for deposition of indium-containing layers (2024-01-02). L'Air Liquide, Société Anonyme pour l'Etude et l'Exploitation des Procédés Georges Claude [FR]. Inventors: Antoine Bruneau [FR], Takashi Ono [JP], Christian Dussarrat [JP].
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Patent 2024
1H NMR Atmosphere Distillation Fever Filtration Indium indium oxide Obstetric Delivery Ozone Pressure Pulse Rate Solvents Vacuum
2
Polymer-Enhanced Solid-State Electrolytes
All chemicals were purchased from commercial sources
and used as received unless mentioned otherwise. Indium foil (thickness
0.1 mm, ≥99.995% trace metals basis) was purchased from Sigma
Aldrich and used as received. Battery-grade lithium foil (thickness
0.1 mm) was purchased from MSE Supplies. The lithium foil was polished
on a polypropylene brush until shiny before use. The solution of LPSCl
and polymer was prepared as follows. LPSCl was dissolved in ethanol
at a concentration of 0.2 g/mL. PEO was dissolved in acetonitrile
and PPO was dissolved in toluene at a concentration of 1, 5, and 10
mg/mL for both polymers. Then, the LPSCl solution and polymer solution
were co-precipitated in centrifuge tubes. The mixture was centrifuged
at 6000 rpm for 10 min. The overall processing time of LPSCl, including
dissolution, precipitation, and separation steps, was controlled to
be within 15 min to minimize the negative effect of EtOH on LPSCl.
The solvent was then discarded, and the precipitate was dried under
a vacuum at room temperature overnight. For the PPO–LPSCl composite,
the sample was further dried at 60 °C for at least 2 h. Finally,
the composites were grounded to yield a gray powder composite. Elemental
analysis results: PEO–LPSCl composites: C 1.00%, H 0.22%, S
57.55% (1 mg/mL PEO solution), C 2.32%, H 0.71%, S 51.21% (5 mg/mL
PEO solution), C 5.88%, H 1.04%, S 50.55% (10 mg/mL PEO solution).
PPO–LPSCl composites: C 2.58%, H 0.33%, S 55.81% (1 mg/mL PPO
solution), C 6.75%, H 1.77%, S 47.90% (5 mg/mL PPO solution), C 9.44%,
H 2.01%, S 46.31% (10 mg/mL PPO solution). According to the elemental
analysis results, the polymer content in the composites is confirmed
as 2, 8, and 12 wt % from the solution–precipitation method
using polymer solutions (i.e., PEO in acetonitrile and PPO in toluene)
of 1, 5, and 10 mg/mL, respectively.
Khomein P., Byeon Y.W., Liu D., Yu J., Minor A.M., Kim H, & Liu G. (2023). Lithium Phosphorus Sulfide Chloride–Polymer Composite via the Solution–Precipitation Process for Improving Stability toward Dendrite Formation of Li-Ion Solid Electrolyte. ACS Applied Materials & Interfaces, 15(9), 11723-11730.
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Publication 2023
(1-hydroxy-1-methyl-3-phenylpropyl)-6,14-endo-ethenotetrahydrooripavine 5-phenanthro(4,5-bcd)pyran-5-one acetonitrile Ethanol Indium Lithium Metals Polymers Polypropylenes Powder Solvents Toluene Vacuum
3
Ionic Conductivity and Lithium Plating/Stripping Performance
Metrohm Autolab
(FRA32M-impedance analysis) was used to measure the ionic conductivity
using a Swagelok-type cell, which was built as follows: indium (100
μm)//sample pellet (1.2 mm)//indium (100 μm). For the
lithium striping–plating performance test, the Swagelok-type
cell was built as Li (100 μm)//sample pellet (1.2 mm)//Li (100
μm). Galvanostatic cycling was performed at a rate of 0.1 mAh/cm2, in which a 0.48 μm thick piece of lithium will be
striped/plated back and forth. The cell voltage was monitored over
time. The rapid decrease in voltage was regarded as a sign of the
dendrite formation across the pellet. The pressure applied for electrochemical
testing was provided by the internal springs of the Swagelok-type
cell, which was estimated to be 0.2 MPa. All electrochemical tests
were performed at 25 ± 2 °C
Khomein P., Byeon Y.W., Liu D., Yu J., Minor A.M., Kim H, & Liu G. (2023). Lithium Phosphorus Sulfide Chloride–Polymer Composite via the Solution–Precipitation Process for Improving Stability toward Dendrite Formation of Li-Ion Solid Electrolyte. ACS Applied Materials & Interfaces, 15(9), 11723-11730.
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Publication 2023
Cells Indium Ions Lithium Natural Springs Pressure
4
Robust fMRI Preprocessing: ICA Denoising
The raw fMRI volumes from each subject were imported in SPM12 and spatially realigned to the subject’s first volume using the least squares approach followed by a rigid body transformation. The subject’s structural T1-weighted image was co-registered to the mean fMRI image through an affine transformation, corrected from intensity biases, and segmented into different tissue types. The forward deformation field parameters estimated in the latter step were used for normalization of the fMRI volumes from the subject’s native space to the standard Montreal Neurological Institute (MNI) space. The realigned and normalized fMRI volumes were smoothed using a 3D Gaussian kernel filter with Full Width at Half Maximum (FWHM) equal to 6 mm and imported in the FSL software. Here, the pre-processed fMRI volumes from each subject were temporally high-pass filtered (cut-off frequency of 0.01 Hz) and entered into a single-subject spatial ICA decomposition using the FSL Multivariate Exploratory Linear Optimized Decomposition into Independent Component (MELODIC) toolbox (https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/MELODIC). Artefactual independent components (ICs) were marked using a semi-automatic spatiotemporal tool recently published by our research group and subsequently regressed out from the fMRI volumes32 . In our study, the choice to use the proposed tool was motivated by its good performances in the detection of noise-related ICs in rs-fMRI data (> 80% of accuracy, sensitivity, and specificity in 32). The tool marks an unknown IC as either artefactual or physiological based on (i) the spatial correlation between its spatial map and those from labeled artefactual ICs, (ii) the proportion of high-frequency content (> 0.1 Hz) in its time series, as assessed via a relative power spectral analysis. ICs with values of spatial correlation or high-frequency power higher than predefined thresholds (identified as optimal in32 ) were marked as artefactual. The results from the automatic IC labeling were subjected to a final careful inspection, which was followed by the removal of noise-related ICs.
The resulting fMRI volumes were subjected to the connectivity and statistical analyses described in the next sections.
In a final quality check, the subjects with a percentage of noise-related ICs > 75% and their siblings were excluded from the analyses, resulting in a dataset composed of 43 twin pairs.
The overall quality of the fMRI dataset was assessed by monitoring the extent of movement artefacts before and after the denoising steps. Specifically, the extent of motion in the original fMRI dataset was measured using framewise displacement (FD), whereas the effects of denoising on motion artefacts were quantified using the FD-DVARS metric, which was compared from before to after pre-processing via paired t-tests.
In the original fMRI dataset, the average FD across included subjects was 0.41 mm (± 0.76 mm) below the commonly used FD censoring threshold of 0.5 mm. Furthermore, our pre-processing pipeline resulted in a significant reduction of FD-DVARS (p < 0.001), which on average decreased from 0.78 (± 0.20) to 0.64 (± 0.21) before ICA, up to 0.60 (± 0.21) after ICA.
Tassi E., Maggioni E., Mauri M., Fagnani C., Agarwal N., Bianchi A.M., Stazi M.A., Nobile M, & Brambilla P. (2023). Environmental effects on brain functional networks in a juvenile twin population. Scientific Reports, 13, 3921.
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Publication 2023
fMRI Histocompatibility Testing Human Body Hypersensitivity Indium Movement Muscle Rigidity physiology Sibling Twins
5
Subclade Test of Skewness for Distinguishing Passive and Driven Trends
To establish whether trends conform to passive or driven processes, we applied a subclade test of skewness45 (link). The test operates by partitioning the total skewness of a trait in a parent group into three main components: skewness between subclades (SCB); skewness within subclades (SCW); and heteroskedasticity-related skewness (SCH; skewness caused by heterogeneity in trait variance in subclades) (Supplementary Figs. 20–24, Extended Data Fig. 3 and Supplementary Table 5). The test quantifies the combined effects of passive and driven processes in terms of the proportional contributions of SCB, SCW and SCH to the total skewness. Such contributions are based upon the normal versus non-normal distributions of mean subclade values relative to the mean value of the parent group and upon the degree of skewness in the subclades (that is, small versus large standard deviations of subclade values within the right tail of the total group). The prevalence of either SCB or SCH suggests passive trends, while the prevalence of SCW suggests driven trends. High proportions of SCW suggest that the overall skewness pattern is replicated in several constituent subclades. This is itself indicative of a driven trend, as a tendency towards higher or lower values will skew not only the ensemble distribution but also the distributions of most or all subclades45 (link),84 (link),88 (link)–90 (link). As an example, consider a parent group with a right-skewed trait (for example, complexity or size). Furthermore, suppose that each constituent subclade shows symmetrically distributed values around its own mean and that the means of the subclade distributions are right-skewed around the parent group’s mean. In this scenario, the right-skewed distribution of the parent group results from a passive trend, as SCB prevails. Now assume that the distribution of each subclade is also right-skewed. In this scenario, SCW prevails, pointing to a driven trend. This would also be the case if the means of the subclades were symmetrically distributed around the parent group’s mean. In a final scenario, suppose that each subclade has symmetrically distributed values and, further, that the variance increases in subclades at the right tail end of the parent group’s distribution (that is, those subclades exhibit a greater spread of values around their own means). In this case, the right-skewed distribution of the parent group is caused by heteroskedasticity (SCH) and, as in the case of SCB, it indicates a passive process45 (link). The test code builds probability density functions for the values of the parent group and those of its subclades and outputs a list of the percentage contributions of SCB, SCH and SCW to the skewness of the parent group. For each index, the total skewness was calculated in e1071. As analyses of skewness are predicated on right-skewed distributions45 (link),84 (link) and because most indices are negatively skewed (except for unstandardized T:L), we transformed those indices by taking their negative logarithms89 (link) before subjecting them to the test. Subclade tests on selected major groups followed identical protocols.
Li Y., Brinkworth A., Green E., Oyston J., Wills M, & Ruta M. (2023). Divergent vertebral formulae shape the evolution of axial complexity in mammals. Nature Ecology & Evolution, 7(3), 367-381.
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Publication 2023
Genetic Heterogeneity Indium Parent Tail

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More about "Indium"

Indium is a versatile element with a wide range of applications in the modern world.
This soft, silvery-white metallic substance with the atomic number 49 is an essential trace mineral that plays a crucial role in various biological processes, including enzyme activation and immune function.
Researchers are actively exploring the diverse applications and potential health effects of indium.
One of the key uses of indium is in the production of electronic devices, such as liquid crystal displays (LCDs) and solar cells.
The element's unique properties, including its low melting point and high electrical conductivity, make it an essential component in these cutting-edge technologies.
Indium's applications extend beyond electronics, with the element also finding use in the manufacture of specialty alloys, coatings, and other industrial products.
Analytical techniques like differential scanning calorimetry (DSC) using equipment like the DSC Q2000, DSC-60, and DSC 822e are often employed to study the thermal properties and behavior of indium-containing materials.
In the biological realm, indium plays a vital role in various enzymatic processes and immune system functions.
Researchers are actively investigating the potential health benefits and side effects of indium exposure, using compounds like oleylamine, oleic acid, and 1-octadecene to study its interactions with living organisms.
PubCompare.ai can be a valuable resource for researchers working with indium, providing AI-driven comparisons of protocols and methods from scientific literature, pre-prints, and patents.
This can help optimize indium-related projects, enhance reproducibility, and ensure the use of the most reliable and accurate techniques, such as those involving the DSC 8000 and 1-dodecanethiol.
By leveraging PubCompare.ai, researchers can stay at the forefront of indium-related discoveries and advancements.