Science AMA Series: We designed a method to quantify how “green” a chemical is; We’re Jane Murray and Samy Ponnusamy, Ask us anything!
Notice bibliographique
Résumé
Our recently published paper in the ACS Sustainable Chemistry & Engineering journal describes a quantitative assessment tool to evaluate chemicals and chemical processes against the 12 Principles of Green Chemistry, using generally accepted industry practices and readily available data sources. This tool, called DOZN, provides a consistent framework for measuring and communicating what’s “greener” about the products labeled as “greener alternatives” and is robust and flexible enough to encompass a diverse product portfolio, from biology to chemistry to materials science. So, feel free to ask us anything about this tool and how it’s currently being implemented at MilliporeSigma, or how you can apply it in your organization. We’ll be back at 1:00 PM Eastern Time (10 am PT, 6 pm UTC) to answer your questions, ask us anything! Dr. Jane Murray: I am the head of Green Chemistry for the Life Science business of Merck KGaA, Darmstadt, Germany, which operates as MilliporeSigma in the U.S. and Canada. I have a background in chemical research—having completed my Ph.D. at the University of York, where I researched green oxidations of organosulfur compounds using hydrogen peroxide. I am a member of the American Chemical Society’s Green Chemistry Institute, Chemical Manufacturer’s Roundtable, the Royal Society of Chemistry and the American Chemical Society. Dr. Ettigounder “Samy” Ponnusamy: I am the Green Chemistry Fellow with the Life Science business of Merck KGaA, Darmstadt, Germany, which operates as MilliporeSigma in the U.S. and Canada. In this role, I manage and expand new green business opportunities, as well as research and develop greener alternatives—including spearheading the DOZN tool that we’ll be talking about on this AMA. I have more than 30 years of experience managing new product developments—from bench scale through product launch—with many products showing sustained growth over time. I earned my Ph.D. from the University of Madras and am the co-author of 30 related scientific articles and holder/co-holder of seven patents. Edit: We forgot to include the link to the paper: http://pubs.acs.org/doi/pdfplus/10.1021/acssuschemeng.6b02399 Edit 2: We’ll be back in an hour to begin answering but wanted to share a link to the 12 Principles of Green Chemistry that we referred to at the top - https://www.acs.org/content/acs/en/greenchemistry/what-is-green-chemistry/principles/12-principles-of-green-chemistry.html Edit 3: Hi everyone, thank you for all of the questions. We’ll be sticking around until 2:30 EST to answer questions, so keep them coming. If you’re interested in learning more about MilliporeSigma’s program, you can go to www.sigma.com/greener Edit 4: Thank you everyone for the great questions! This was both of our first times on Reddit and we appreciate the informative and engaging discussion - hopefully you did as well. We’re sorry if we weren’t able to get to your question but we hope to be back here sometime soon. If you have time, feel free to take a look at the links we shared above and throughout our answers. If you’d like to see an example of our DOZN scoring for a real product, you can see it here: http://www.sigmaaldrich.com/catalog/product/sigma/a7005 If you have any other feedback or questions, please continue to post. We’ll continue to revisit this thread and may even answer a few more questions. Thank you again!
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,013 | 0,023 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,007 | 0,004 |
| Études des sciences et des technologies | 0,003 | 0,003 |
| Communication savante | 0,007 | 0,007 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,003 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,041 | 0,030 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».