Hydrogenation
This reaction is widely used in the production of various chemicals, fuels, and pharmaceuticals.
Hydrogenation can be catalyzed by metal catalysts, typically transition metals, and can occur under a variety of conditions, including high temperature and pressure.
The resulting products are often more saturated and stable, with different physical and chemical properties compared to the original compounds.
Understanding the principles and applications of hydrogenation is crucial in fields like organic chemistry, chemical engineering, and biofuel development.
Researchers can optimize their hydrogenation workflows and unlock new insights using AI-driven tools like PubCompare.ai, which can help locate the best protocols from literature, preprints, and patents, and enhance reproducibility through poweful analysis.
Most cited protocols related to «Hydrogenation»
Ultra-processed foods, which are the focus of interest in this study, as previously mentioned, are formulations of low-cost ingredients, many of non-culinary use, that result from a sequence of industrial processes. Processes underlying the manufacture of ultra-processed foods start with the extraction of substances existing in intact foods, such as oils, fats, sugars, starches and protein. Intermediate processes may involve hydrolysis, hydrogenation and other chemical modifications of the extracted substances. Other steps include the assembling of modified (eg, hydrogenated oils) and unmodified (eg, sugar) substances using processes such as extrusion and prefrying, the addition of additives and sophisticated packaging with the frequent employment of novel synthetic materials.10 12 (link)
Food items were ultimately classified as ultra-processed if they contained ingredients found exclusively in these products. These ingredients are substances derived from foods but of non-culinary use (eg, protein isolates, modified starches, hydrogenated or interesterified oils) and classes of additives with cosmetic functions (eg, colourants, flavourings, artificial sweeteners, emulsifiers, thickeners and bleaching, bulking, firming, gelling, glazing, foaming and carbonating agents). The presence or absence of these ingredients was identified from auxiliary AUSNUT data sources (Food details file and Food recipe file)21 and from list of ingredients obtained from food packages or from company websites (see
For all food items judged to be a culinary preparation, the recipes were disaggregated using the AUSNUT 2011–2013 Food Recipe File,21 enabling the classification of composite foods into all NOVA food groups. A total of 2585 (45%) food codes were subject to disaggregation, and this process was continued until all ingredients were single food items.
To classify all food items, two experts with Australian food and dietary intake knowledge applied the NOVA system to the AUSNUT 2011–2013. All classifications were checked by another two independent food assessment experts, and where classification discrepancies arose, these were discussed until consensus was reached among all researchers. The NOVA system was applied to the AUSNUT classification system that considers a major (two-digit), submajor (three-digit) or minor (five-digit) food group. The survey ID (eight-digit) assigned to each food item was used when it was not possible to discriminate the degree of food processing within a minor group (
When the classification of a food item was not clear (eg, cake or cupcake, honey, commercial or homemade), the conservative alternative was chosen (homemade in this case, and thus disaggregated). Additional procedures were applied to classify breads with generic food item descriptions based on the sampling details information comprised in the AUSNUT ‘Food details file’. Unlike other countries, many commercially produced breads in Australia are processed rather than ultra-processed, that is, their ingredients do not include neither food substances of no culinary use nor cosmetic additives. Of the 62 generic bread codes where the NOVA classification was not easily apparent, there were two generic bread codes that contributed the most to total bread energy intake (25% combined): (1) bread, from white flour, commercial; (2) bread roll, from white flour, commercial. They were classified as ultra-processed foods since the samples that composed the AUSNUT 2011–2013 were mostly of mass-produced branded breads with cosmetic additives. All the remaining infrequent breads were classified as processed as the conservative hypothesis (see details in the
A four-stage process was undertaken to identify the ultra-processed foods from both the adult and the youth FFQs. First, all food items in the FFQs across different waves of data collection were complied. Food items that were nearly identical between FFQs but were presented with minor differences were captured as separate items (e.g., ‘Cold breakfast cereal (1 bowl)’ and ‘Cold breakfast cereal (1 serving)’). This was done to make sure that no food item was overlooked. FFQs from every 4 years of the NHS-I (1986–2010), the NHS-II (1991–2015), the HPFS (1986–2014), from 1996, 1998, 2001 for GUTS-I and from 2004, 2006, 2008, 2011 for GUTS-II were used.
Second, three researchers working independently assigned foods in the adult (N.K, S.R, E.M) and the youth (N.K, M.D, E.M) cohorts to one of the four NOVA groups based on their grade of processing – unprocessed/minimally processed foods (G1), processed culinary ingredients (G2), processed foods (G3) and ultra-processed foods (G4). Food assignment was guided by the definition, examples and supplementary material published by the proponents of the NOVA classification(1 (link)). Categorisation was an iterative process requiring the review of the original FFQs used at each wave of data collection to contextualise food items within the larger food lists. Food preparations made from multiple ingredients or different food items that were presented jointly in the FFQ were not disaggregated into their different components. Additionally, the nutrient profile of food items, their actual amounts consumed by the study participants or participant demographics were not considered at any point in the categorisation process. Instead, the original food item as it was listed in the FFQ was categorised in its entirety.
At the third stage, categorisation between researchers was triangulated. Food items for which there was consensus in the categorisation among all researchers were assigned to their NOVA group. A food item was flagged for further scrutiny and shortlisted in case a researcher was unable to assign it to a NOVA group or in cases of disagreement in categorisation by any two researchers.
At stage four, an expert panel comprising of three senior nutrition epidemiologists (F.F.Z; T.F; Q.S) with substantial experience working with the dietary intake in these cohorts, was convened to review and discuss the categorisation of the short-listed products. All discussions were additionally informed by the following resources:
Consultations with the research dietitians. The team of research dietitians, led by L.S, was responsible for overseeing the collection of dietary data and for ascertaining the nutrient composition of food items across all Harvard cohorts. They shared their insights obtained from gathering supplementary data, tracking new and reformulated products available in the supermarket, and conducting multiple pilot studies with cohort participants.
Cohort-specific documents. These resources provided more insight into the extent of processing of certain FFQ food items by highlighting information on the specific ingredients used in recipes and food preparations, the proportion by weight of individual ingredients to the final recipe or a more detailed description of food items (whether the food was canned or salted or boiled, the brand name of certain packaged foods, etc.).
Supermarket scans. The ingredient lists of the first five brands of specific products that were displayed on the Walmart website in 2019 and 2020 were scrutinised. They served as a proxy for establishing the level of processing for a small proportion of food items for which limited information was available from the resources listed above.
Most recents protocols related to «Hydrogenation»
Example 100
To a solution of compound 487 (4 g, 9.85 mmol) in MeOH (40 mL) was added Pd/C (0.4 g, 10 wt %) in a hydrogenation bottle. The mixture was stirred under 1 atm H2 overnight, filtered through Celite (filter aid), and the filtrate was concentrated to afford compound 488 (3.6 g, yield-100%). ESI m/z: calcd for C21H35N2O4 [M+H]+: 379.25, found 379.25.
Example 123
In a hydrogenation bottle, Pd/C (0.093 g, 10 wt %) was added to a solution of compound 530 (0.93 g, 1.27 mmol) in EtOAc (20 mL). The mixture was shaken overnight under 1 atm H2 then filtered through Celite (filter aid), the filtrate was concentrated to afford compound 531 (0.57 g, 81%) and used in the next step without further purification. ESI m/z calcd for C28H48N3O8 [M+H]+: 554.34, found 554.34.
Example 104
To a solution of compound 491 (100 mg, 0.236 mmol) in MeOH (4 mL) was added Pd/C (10 mg, 10 wt %) in a hydrogenation bottle. The mixture was stirred under 1 atm H2 overnight, filtered through Celite (filter aid), and the filtrate was concentrated to afford the title compound (92.9 mg, —100% yield). ESI m/z calcd for C21H36N3O4 [M+H]+: 394.26, found 394.26.
Example 75
To a solution of tert-butyl 3-(2-(2-(dibenzylamino)ethoxy)ethoxy) propanoate (20.00 g, 48.36 mmol, 1.0 eq.) in THF (30 mL) and MeOH (60 mL) was added Pd/C (2.00 g, 10 wt %, 50% wet) in a hydrogenation bottle. The mixture was shaken overnight, filtered through Celite (filter aid), and the filtrate was concentrated to afford a colourless oil (10.58 g, 93.8% yield). MS ESI m/z calcd for C11H24NO4 [M+H]+ 234.1627, found 234.1810.
Example 154
Compound 667 (3.0 g, 13.4 mmol, 1.0 eq.) and Pd/C (0.3 g, 10% Pd/C, 50% wet) were mixed with HCl (3 mL) and MeOH (100 mL) in a hydrogenation bottle and shaken at 100 psi H2 atmosphere for 5 h. Then the mixture was filtered over Celite and the filtrate was concentrated to give the title compound as a yellow solid (2.1 g, 98% yield). 1H NMR (400 MHz, D2O) δ 3.33 (d, J=4.6 Hz, 2H), 3.27 (s, 2H), 2.79 (s, 6H).
Top products related to «Hydrogenation»
More about "Hydrogenation"
This reaction is widely employed in the production of various chemicals, fuels, and pharmaceuticals.
The process of hydrogenation can be catalyzed by transition metal catalysts and can occur under a range of conditions, including high temperature and pressure.
The resulting products are often more saturated and stable, with different physical and chemical properties compared to the original compounds.
Understanding the principles and applications of hydrogenation is crucial in fields like organic chemistry, chemical engineering, and biofuel development.
Researchers can optimize their hydrogenation workflows and unlock new insights using AI-driven tools like PubCompare.ai, which can help locate the best protocols from literature, preprints, and patents, and enhance reproducibility through powerful analysis.
Hydrogenation is related to other chemical processes such as hydrogenolysis, which involves the cleavage of carbon-heteroatom bonds, and hydroformylation, which adds an aldehyde group to an alkene.
Software tools like AutoDock Tools, AutoDock Vina 1.1.2, SYBYL-X 2.0, and PyMOL 2.5 can be used to model and analyze the interactions involved in hydrogenation reactions.
Analytical techniques like GC-2014 and HP-5 column chromatography can be employed to characterize the products of hydrogenation reactions.
The PCTPro-2000 software can be used to predict the physical and chemical properties of hydrogenated compounds.
By leveraging the insights gained from the MeSH term description and incorporating relevant synonyms, related terms, and key subtopics, researchers can enhance their understanding of hydrogenation and optimize their workflows using AI-driven tools like PubCompare.ai.
This can lead to new discoveries and advancements in fields such as organic chemistry, chemical engineering, and biofuel development.