Carlos Angel-Jijón

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Carlos Angel-Jijón

Carlos Angel-Jijón

@CarlosAngel92

PhD. Student in Orgánica Chemistry at the UAEH. I like play soccer, swim. I am from San Luis Acatlán, Guerrero.

Katılım Eylül 2021
154 Takip Edilen65 Takipçiler
Carlos Angel-Jijón
Carlos Angel-Jijón@CarlosAngel92·
@MezaGordillo @LatinXChem Hi! Sometimes. Normally, We would think that a ratio 1:1 ( azide: alkyne) is best. But a ratio 1:1.2 alkyne/ azide is used to obtain good yields. Because the mechanism starts with the activation of alkyne and the azide attack to this complex.
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Rocio Meza Gordillo
Rocio Meza Gordillo@MezaGordillo·
@CarlosAngel92 @LatinXChem Hi Carlos, congratulations! Based on your experience, do you consider that using different molar ratios of azide and alkyne could yield different products depending on this ratio?
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Carlos Angel-Jijón
Carlos Angel-Jijón@CarlosAngel92·
@OrgSynLujanLab @LatinXChem Hello! Yes. Because by blocking the acid group, the amino acid prefers protein synthesis (it starts at the unprotected amino end). It has been decided to join the indole to the carboxylate to direct the amino acid towards protein synthesis. Modifications can be made to indol ring
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Lujan group
Lujan group@OrgSynLujanLab·
@CarlosAngel92 @LatinXChem Hello Carlos, this is Armando Luján from Cinvestav MX. Beyond the general interest in expanding chemical space by combining an amino acid with an indole, is there a specific reason why attaching an indole to the carboxylate end of an amino acid is particularly important?
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Emmanuel Campos
Emmanuel Campos@MannyCampos1998·
Hi @LatinXChem, presenting my work 'Synthesis of dinuclear palladium(II) and gold(I) complexes integrating N-heterocyclic carbenes functionalized with thioether groups' at #LatinXChem #LatinXChemOrg #Org098
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Angel Dionicio
Angel Dionicio@dionicio1305·
Hola @LatinXChem presento mi trabajo el cual es titulado Estudio in silico de docking molecular para la búsqueda de potenciales inhibidores de PD-1/PD-L1 para el tratamiento contra el cáncer en #LatinXChem24 #LatinXChemBio #Bio50
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Jorge Bravo Abad
Jorge Bravo Abad@bravo_abad·
Multi-task learning for enhanced quantum property predictions Characterizing the properties of many-body quantum systems is a fundamental task in quantum physics and quantum computing. However, this process becomes increasingly challenging as system size grows, requiring extensive data from measurements across multiple particles. A recent study by Wu an coauthors introduces  a machine learning model that addresses this challenge by leveraging short-range correlations—relationships between particles that are close to one another. The researchers developed a multi-task neural network that learns from local measurements and predicts global quantum properties, such as entanglement or phase transitions. Unlike traditional models that predict one property at a time, multi-task networks can handle multiple properties simultaneously, improving both accuracy and efficiency. 📲 nature.com/articles/s4146…
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