Pub Date : 2025-04-13DOI: 10.1021/acssuschemeng.4c08711
Phuc H. Pham, Sean P. Keyser, Matthew Ticknor, Keenan W. Wyatt, Elisa M. Miller, Stephen Barlow, Seth R. Marder, Oana R. Luca
This work describes the use of electrochemically generated superoxide (with air as the source of O2) at carbon electrodes as a reagent for the depolymerization of polyesters. We report the electricity-driven selective conversion of these common ester-based wastes into their foundational carboxylate and alkoxide building blocks. The results pave the way for an electrochemical approach to the recovery of molecular materials from ester wastes that uses air and electricity as key reagents for material recycling.
{"title":"Air-Enabled Electricity-Driven Depolymerization of Polyesters","authors":"Phuc H. Pham, Sean P. Keyser, Matthew Ticknor, Keenan W. Wyatt, Elisa M. Miller, Stephen Barlow, Seth R. Marder, Oana R. Luca","doi":"10.1021/acssuschemeng.4c08711","DOIUrl":"https://doi.org/10.1021/acssuschemeng.4c08711","url":null,"abstract":"This work describes the use of electrochemically generated superoxide (with air as the source of O<sub>2</sub>) at carbon electrodes as a reagent for the depolymerization of polyesters. We report the electricity-driven selective conversion of these common ester-based wastes into their foundational carboxylate and alkoxide building blocks. The results pave the way for an electrochemical approach to the recovery of molecular materials from ester wastes that uses air and electricity as key reagents for material recycling.","PeriodicalId":25,"journal":{"name":"ACS Sustainable Chemistry & Engineering","volume":"37 1","pages":""},"PeriodicalIF":8.4,"publicationDate":"2025-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143827216","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-11DOI: 10.1021/acssuschemeng.5c00524
Vinothkumar Vinayagam, Subir Kumar Sadhukhan, Dhurwasulu Baledi, Nooka Raju Anisetti, Sai Kilari, Hema Sundar Naveen Babu Bathula, Vijayasaradhi Sivalenka, Srikanth reddy Surukonti, Siva Kundrapu, Bala Sai Pampana
A general, convenient, and versatile protocol for the Pd-catalyzed C(sp2)–N cross-coupling has been developed that enabled coupling of several types of amine nucleophiles with aryl/heteroaryl halides in water at ambient temperature under micellar catalysis conditions. The commercially available plant-based natural saponin, a known surfactant, served as micellar catalysis promoting the C(sp2)–N cross-coupling reaction effectively with a wide range of amine derivatives such as aliphatic amines, aromatic amines, amides, carbamates, sulfonamides, and ureas, each of which previously required a different catalyst system to achieve optimal results. Also, the saponin-mediated micellar catalysis system effectively promoted cross-coupling of an array of heteroarene substrates, which are otherwise challenging substrates, as the heteroatom in a N-heteroarene has the ability to displace phosphine ligands from the metal center and can deactivate the catalyst system. The attractive features of this protocol are the use of water as a green solvent, the in situ generation of a micellar-catalysis system from natural saponin, promoting the reaction at room temperature in a short period, and notably the ability to recycle aqueous reaction medium containing still an active micellar-catalysis. The FE-SEM, EDS, and DLS analysis revealed that the saponin formed an aggregate in the shape of a sphere, incorporating the Pd-catalyst, and resulting in significantly increasing the reactivity. We also demonstrated that this operationally simple procedure can be successfully applied to a broad range of substrates including some API intermediates, and is amenable to scale-up.
{"title":"Green Approach for Pd-Catalyzed C–N Bond Formation with Weak Nitrogen Nucleophiles Using Saponin-Based Micellar Catalysis: Arylation of Anilines, Amides, Carbamates, Ureas, and Sulfonamides","authors":"Vinothkumar Vinayagam, Subir Kumar Sadhukhan, Dhurwasulu Baledi, Nooka Raju Anisetti, Sai Kilari, Hema Sundar Naveen Babu Bathula, Vijayasaradhi Sivalenka, Srikanth reddy Surukonti, Siva Kundrapu, Bala Sai Pampana","doi":"10.1021/acssuschemeng.5c00524","DOIUrl":"https://doi.org/10.1021/acssuschemeng.5c00524","url":null,"abstract":"A general, convenient, and versatile protocol for the Pd-catalyzed C(sp<sup>2</sup>)–N cross-coupling has been developed that enabled coupling of several types of amine nucleophiles with aryl/heteroaryl halides in water at ambient temperature under micellar catalysis conditions. The commercially available plant-based natural saponin, a known surfactant, served as micellar catalysis promoting the C(sp<sup>2</sup>)–N cross-coupling reaction effectively with a wide range of amine derivatives such as aliphatic amines, aromatic amines, amides, carbamates, sulfonamides, and ureas, each of which previously required a different catalyst system to achieve optimal results. Also, the saponin-mediated micellar catalysis system effectively promoted cross-coupling of an array of heteroarene substrates, which are otherwise challenging substrates, as the heteroatom in a <i>N</i>-heteroarene has the ability to displace phosphine ligands from the metal center and can deactivate the catalyst system. The attractive features of this protocol are the use of water as a green solvent, the in situ generation of a micellar-catalysis system from natural saponin, promoting the reaction at room temperature in a short period, and notably the ability to recycle aqueous reaction medium containing still an active micellar-catalysis. The FE-SEM, EDS, and DLS analysis revealed that the saponin formed an aggregate in the shape of a sphere, incorporating the Pd-catalyst, and resulting in significantly increasing the reactivity. We also demonstrated that this operationally simple procedure can be successfully applied to a broad range of substrates including some API intermediates, and is amenable to scale-up.","PeriodicalId":25,"journal":{"name":"ACS Sustainable Chemistry & Engineering","volume":"183 1","pages":""},"PeriodicalIF":8.4,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143819702","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-11DOI: 10.1021/acssuschemeng.5c01299
Guanhua Ren, Wei Yang, Jingjing Bao, Yu Shi, Licheng Sun, Zhengyu Mo, Min Du
Thermo-electrochemical cells (TECs) are a promising technology that can convert waste heat into electrical energy, offering an effective way to improve energy efficiency and mitigate greenhouse gas emissions. However, the power generation of TECs is often limited by the inefficiency of the electrodes. In this article, we design a conductive polymer/multidimensional carbon composite on graphite felt electrodes for TECs. The conductive polymer and multidimensional carbon composite enhance the effective surface area and improve the accessibility of reactive sites for redox reactions, addressing the difficulties of electron transfer to the surface and interface from the electrode. In addition, the open porous structure of the graphite felt electrode helps overcome the diffusion limitation of redox ions. An electrocatalytic relationship between the electrode properties and the redox reaction activity was studied. The results indicate that the multidimensional carbon structure facilitates mass transport with an increased ion diffusion coefficient from 7.71 × 10–12 to 7.29 × 10–11 m2/s, while the deposition of polyaniline effectively improves the intrinsic activity of the electrode with a 1.6- to 1.7-fold increase in the apparent rate constant for the redox reaction. The TEC with the prepared electrode delivers a maximum power density of 1664 mW/m2 and a Carnot-relative efficiency of 1.97%, which are 9.2 and 8.9 times higher than that with the bare graphite felt electrode. In all, this work provides a strategy for the design of efficient electrodes in TECs, guiding future research on efficient systems for waste heat conversion.
{"title":"Conductive Polymer/Multidimensional Carbon Composite on Graphite Felt Electrodes for Liquid Thermo-Electrochemical Cells","authors":"Guanhua Ren, Wei Yang, Jingjing Bao, Yu Shi, Licheng Sun, Zhengyu Mo, Min Du","doi":"10.1021/acssuschemeng.5c01299","DOIUrl":"https://doi.org/10.1021/acssuschemeng.5c01299","url":null,"abstract":"Thermo-electrochemical cells (TECs) are a promising technology that can convert waste heat into electrical energy, offering an effective way to improve energy efficiency and mitigate greenhouse gas emissions. However, the power generation of TECs is often limited by the inefficiency of the electrodes. In this article, we design a conductive polymer/multidimensional carbon composite on graphite felt electrodes for TECs. The conductive polymer and multidimensional carbon composite enhance the effective surface area and improve the accessibility of reactive sites for redox reactions, addressing the difficulties of electron transfer to the surface and interface from the electrode. In addition, the open porous structure of the graphite felt electrode helps overcome the diffusion limitation of redox ions. An electrocatalytic relationship between the electrode properties and the redox reaction activity was studied. The results indicate that the multidimensional carbon structure facilitates mass transport with an increased ion diffusion coefficient from 7.71 × 10<sup>–12</sup> to 7.29 × 10<sup>–11</sup> m<sup>2</sup>/s, while the deposition of polyaniline effectively improves the intrinsic activity of the electrode with a 1.6- to 1.7-fold increase in the apparent rate constant for the redox reaction. The TEC with the prepared electrode delivers a maximum power density of 1664 mW/m<sup>2</sup> and a Carnot-relative efficiency of 1.97%, which are 9.2 and 8.9 times higher than that with the bare graphite felt electrode. In all, this work provides a strategy for the design of efficient electrodes in TECs, guiding future research on efficient systems for waste heat conversion.","PeriodicalId":25,"journal":{"name":"ACS Sustainable Chemistry & Engineering","volume":"37 1","pages":""},"PeriodicalIF":8.4,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143819698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-11DOI: 10.1021/acssuschemeng.5c01636
Yiwen Bao, Jheng-Han Tsai, Jen-Yi Huang
As the most common method for juice concentration, evaporation is, however, a process with high energy requirements. In this study, microbubbles (MBs) generated with air at different temperatures (22, 45, 70 °C) were introduced to assist in water evaporation at 40, 55, and 70 °C, with the goal of improving the energy efficiency of juice concentration. The number density of MBs was inversely affected by the liquid temperature. Infusing MBs into apple juice considerably enhanced water evaporation, but the enhancing effect became less significant as the juice temperature increased, ranging from 76% to 104% at 40 °C to approximately 37% at 70 °C. Moreover, increasing air temperature only improved the performance of MB-assisted evaporation to a minor (16%) or negligible extent. Computational fluid dynamics simulations of bubbly flow demonstrated that MBs enhanced water evaporation by improving mixing and, thus, heat transfer in the liquid, and the predicted results reasonably agreed with the experiments. Although air MBs did not deteriorate the sensory qualities of treated juice, they caused marked oxidation of nutrients. Replacing air with nitrogen for MB infusion effectively preserved the nutritional values of the juice. This study demonstrates a promising and easy-to-implement technology that can help the food and beverage industries innovate conventional concentration processes.
{"title":"Enhancing Water Evaporation during Apple Juice Concentration Using Microbubbles","authors":"Yiwen Bao, Jheng-Han Tsai, Jen-Yi Huang","doi":"10.1021/acssuschemeng.5c01636","DOIUrl":"https://doi.org/10.1021/acssuschemeng.5c01636","url":null,"abstract":"As the most common method for juice concentration, evaporation is, however, a process with high energy requirements. In this study, microbubbles (MBs) generated with air at different temperatures (22, 45, 70 °C) were introduced to assist in water evaporation at 40, 55, and 70 °C, with the goal of improving the energy efficiency of juice concentration. The number density of MBs was inversely affected by the liquid temperature. Infusing MBs into apple juice considerably enhanced water evaporation, but the enhancing effect became less significant as the juice temperature increased, ranging from 76% to 104% at 40 °C to approximately 37% at 70 °C. Moreover, increasing air temperature only improved the performance of MB-assisted evaporation to a minor (16%) or negligible extent. Computational fluid dynamics simulations of bubbly flow demonstrated that MBs enhanced water evaporation by improving mixing and, thus, heat transfer in the liquid, and the predicted results reasonably agreed with the experiments. Although air MBs did not deteriorate the sensory qualities of treated juice, they caused marked oxidation of nutrients. Replacing air with nitrogen for MB infusion effectively preserved the nutritional values of the juice. This study demonstrates a promising and easy-to-implement technology that can help the food and beverage industries innovate conventional concentration processes.","PeriodicalId":25,"journal":{"name":"ACS Sustainable Chemistry & Engineering","volume":"44 7 1","pages":""},"PeriodicalIF":8.4,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143819749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-11DOI: 10.1021/acssuschemeng.5c00225
Noman Raza Sial, Muhammad Abdul Qyyum, Apoorv Lal, Fengqi You
Bitcoin, the pioneering decentralized currency, has transformed global finance. However, its expanding network drives greenhouse gas emissions, water use, and land consumption, posing sustainability challenges. This study introduces a novel methodological framework to assess these impacts across participating nations, integrating the ReCiPE 2016 midpoint (H) method for life cycle impact assessment of offsite environmental footprints and mathematical modeling for onsite environmental footprints, using open LCA and the Ecoinvent v3.10 database in a cradle-to-gate approach. The findings reveal that the United States, China, and Kazakhstan, responsible for 65% of global Bitcoin mining, are the largest contributors to its environmental impact. The United States records a water footprint of 419 million cubic meters under high computational load, enough to meet the annual water needs of Antigua and Barbuda, Barbados, and Bhutan. China leads with a 900 km2 land footprint, while Kazakhstan emits over 25 MtCO2e greenhouse gas emissions, driven by coal reliance. These findings offer policymakers region-specific insights to balance Bitcoin’s economic benefits with its environmental costs, emphasizing the need for technological advancements and sustainable energy shifts. The study also calls for future research into pinpointing the Bitcoin miners’ locations and the true computational mix of the global Bitcoin network.
{"title":"Assessing the Environmental Footprint of Bitcoin: A Comprehensive Analysis of Water, Land, and Carbon Impacts","authors":"Noman Raza Sial, Muhammad Abdul Qyyum, Apoorv Lal, Fengqi You","doi":"10.1021/acssuschemeng.5c00225","DOIUrl":"https://doi.org/10.1021/acssuschemeng.5c00225","url":null,"abstract":"Bitcoin, the pioneering decentralized currency, has transformed global finance. However, its expanding network drives greenhouse gas emissions, water use, and land consumption, posing sustainability challenges. This study introduces a novel methodological framework to assess these impacts across participating nations, integrating the ReCiPE 2016 midpoint (H) method for life cycle impact assessment of offsite environmental footprints and mathematical modeling for onsite environmental footprints, using open LCA and the Ecoinvent v3.10 database in a cradle-to-gate approach. The findings reveal that the United States, China, and Kazakhstan, responsible for 65% of global Bitcoin mining, are the largest contributors to its environmental impact. The United States records a water footprint of 419 million cubic meters under high computational load, enough to meet the annual water needs of Antigua and Barbuda, Barbados, and Bhutan. China leads with a 900 km<sup>2</sup> land footprint, while Kazakhstan emits over 25 MtCO<sub>2</sub>e greenhouse gas emissions, driven by coal reliance. These findings offer policymakers region-specific insights to balance Bitcoin’s economic benefits with its environmental costs, emphasizing the need for technological advancements and sustainable energy shifts. The study also calls for future research into pinpointing the Bitcoin miners’ locations and the true computational mix of the global Bitcoin network.","PeriodicalId":25,"journal":{"name":"ACS Sustainable Chemistry & Engineering","volume":"117 1","pages":""},"PeriodicalIF":8.4,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143822827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-11DOI: 10.1021/acssuschemeng.4c10709
Yian Chen, Hakan Olcay, Eric C.D. Tan, Sean P. Woodworth, Joel Miscall, Adewale Aromolaran, Patrick O. Saboe, Jeffrey G. Linger, Gregg T. Beckham
During the production of many bio-based chemicals from fermentation and enzymatic processes, product separations frequently represent the most expensive and energy-intensive unit operations in an integrated process, often due to the low concentrations of target bioproducts. In this study, we integrated high-pressure reverse osmosis (HPRO) to concentrate an exemplary fermentation product, butyric acid, prior to downstream extraction. Through both modeling and experimental measurements, we identified the major factors limiting the maximum achievable concentration factor (CF) of 4.0 for butyric acid concentration with an HPRO membrane compared to the 2.6–3.2 range for conventional reverse osmosis (RO) membranes. The resulting concentrated aqueous stream underwent liquid–liquid extraction with an organic solvent and distillation for butyric acid purification and solvent recycling. The integration of HPRO product concentration into an in situ product recovery (ISPR) process leads to >5-fold increase in the final butyric acid concentration in the organic phase, and a concomitant 76% reduction in organic solvent usage. These improvements lead to an estimated 53 and 46% reduction in ISPR butyric acid production cost and greenhouse gas (GHG) emissions, respectively, considerably exceeding the process performance when integrating conventional RO product concentration. Overall, the integration of an HPRO membrane for product concentration enables more economical and sustainable bioproduct recovery from dilute aqueous streams.
{"title":"Carboxylic Acid Concentration in Downstream Bioprocessing Using High-Pressure Reverse Osmosis","authors":"Yian Chen, Hakan Olcay, Eric C.D. Tan, Sean P. Woodworth, Joel Miscall, Adewale Aromolaran, Patrick O. Saboe, Jeffrey G. Linger, Gregg T. Beckham","doi":"10.1021/acssuschemeng.4c10709","DOIUrl":"https://doi.org/10.1021/acssuschemeng.4c10709","url":null,"abstract":"During the production of many bio-based chemicals from fermentation and enzymatic processes, product separations frequently represent the most expensive and energy-intensive unit operations in an integrated process, often due to the low concentrations of target bioproducts. In this study, we integrated high-pressure reverse osmosis (HPRO) to concentrate an exemplary fermentation product, butyric acid, prior to downstream extraction. Through both modeling and experimental measurements, we identified the major factors limiting the maximum achievable concentration factor (CF) of 4.0 for butyric acid concentration with an HPRO membrane compared to the 2.6–3.2 range for conventional reverse osmosis (RO) membranes. The resulting concentrated aqueous stream underwent liquid–liquid extraction with an organic solvent and distillation for butyric acid purification and solvent recycling. The integration of HPRO product concentration into an <i>in situ</i> product recovery (ISPR) process leads to >5-fold increase in the final butyric acid concentration in the organic phase, and a concomitant 76% reduction in organic solvent usage. These improvements lead to an estimated 53 and 46% reduction in ISPR butyric acid production cost and greenhouse gas (GHG) emissions, respectively, considerably exceeding the process performance when integrating conventional RO product concentration. Overall, the integration of an HPRO membrane for product concentration enables more economical and sustainable bioproduct recovery from dilute aqueous streams.","PeriodicalId":25,"journal":{"name":"ACS Sustainable Chemistry & Engineering","volume":"17 1","pages":""},"PeriodicalIF":8.4,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143822826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-10DOI: 10.1021/acssuschemeng.5c00976
Xinran Liu, Yucai He, Cuiluan Ma, Mingming Zheng
2,5-Furandicarboxylic acid (FDCA) is an essential biobased dibasic acid with a structure similar to that of the petroleum-based bulk chemical terephthalic acid. In this research, a highly effectual catalytic process was acquired to transform waste bread (WB) into FDCA in a one-pot, three-step approach. First, 5-hydroxymethylfurfural (24.8 wt % yield) was acquired from WB (40 g/L) in the deep eutectic solvent (DES) choline chloride:lactic acid (ChCl:LA)-H2O (ChCl:LA, 15 wt %) medium for 15 min at 180 °C. The WB-derived HMF was oxidized to 2,5-furandicarboxaldehyde (97.4% yield) using Escherichia coli pRSFDuet-GOase within 5 h. Afterward, WB-derived DFF was transformed to FDCA (0.291 g of FDCA per g of WB) using immobilized lipase powder CL (CL-IM) in tert-butanol:ethyl acetate (t-BuOH:EtOAc = 1:1, v/v) at 40 °C for 9 h. This chemobiological route from food waste to FDCA afforded a new idea for valorization of waste into valuable biobased chemicals in a green and effectual way.
{"title":"Efficient Production of 2,5-Furandicarboxylic Acid via Chemobiocatalytic Sequential Catalysis of Bread Waste in a One-Pot, Three-Step Process Manner in a Benign Reaction System","authors":"Xinran Liu, Yucai He, Cuiluan Ma, Mingming Zheng","doi":"10.1021/acssuschemeng.5c00976","DOIUrl":"https://doi.org/10.1021/acssuschemeng.5c00976","url":null,"abstract":"2,5-Furandicarboxylic acid (FDCA) is an essential biobased dibasic acid with a structure similar to that of the petroleum-based bulk chemical terephthalic acid. In this research, a highly effectual catalytic process was acquired to transform waste bread (WB) into FDCA in a one-pot, three-step approach. First, 5-hydroxymethylfurfural (24.8 wt % yield) was acquired from WB (40 g/L) in the deep eutectic solvent (DES) choline chloride:lactic acid (ChCl:LA)-H<sub>2</sub>O (ChCl:LA, 15 wt %) medium for 15 min at 180 °C. The WB-derived HMF was oxidized to 2,5-furandicarboxaldehyde (97.4% yield) using <i>Escherichia coli</i> pRSFDuet-GOase within 5 h. Afterward, WB-derived DFF was transformed to FDCA (0.291 g of FDCA per g of WB) using immobilized lipase powder CL (CL-IM) in <i>tert</i>-butanol:ethyl acetate (<i>t-</i>BuOH:EtOAc = 1:1, v/v) at 40 °C for 9 h. This chemobiological route from food waste to FDCA afforded a new idea for valorization of waste into valuable biobased chemicals in a green and effectual way.","PeriodicalId":25,"journal":{"name":"ACS Sustainable Chemistry & Engineering","volume":"66 1","pages":""},"PeriodicalIF":8.4,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143819750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-10DOI: 10.1021/acssuschemeng.5c01301
Marvellous Oluwaferanmi Faluyi, Sibel Irmak
The Northeastern United States is a heavily forested area with an increasing demand for energy because of its high population. The forests are promising resources for fulfilling this need. This research was designed to assess the utilization of various forest-based biomass (hardwoods, softwoods, and invasive plant species) for hydrogen production by hydrothermal gasification under mild gasification conditions (250 °C for 90 min). Total organic carbon, carbohydrate content, and lignin breakdown components of the biomass feeds were compared and linked to the overall gasification performance and hydrogen production yield for the forest-based biomass studied. The gasification of the biomass hydrolysates in the presence of a carbon-supported 10% Pt catalyst resulted in gas mixtures that were composed of more than 90% hydrogen. It was observed that mostly carbohydrate-derived compounds were consumed to produce gaseous products, while lignin-derived compounds in the biomass hydrolysates were not very reactive in the hydrothermal gasification reactions. Softwoods (eastern hemlock, spruce, and loblolly pine) produced more gaseous products than hardwoods studied (black walnut and soft maple). The average of the total gas mixture produced from hardwoods (385 ± 15 mL) was lower than that of softwoods (543 ± 25 mL). The gasification performance of invasive biomass, Japanese honeysuckle, and sumac were the same as softwoods, while autumn olive was between softwoods and hardwoods.
{"title":"Hydrothermal Conversion of Forest-Based Biomass to Hydrogen","authors":"Marvellous Oluwaferanmi Faluyi, Sibel Irmak","doi":"10.1021/acssuschemeng.5c01301","DOIUrl":"https://doi.org/10.1021/acssuschemeng.5c01301","url":null,"abstract":"The Northeastern United States is a heavily forested area with an increasing demand for energy because of its high population. The forests are promising resources for fulfilling this need. This research was designed to assess the utilization of various forest-based biomass (hardwoods, softwoods, and invasive plant species) for hydrogen production by hydrothermal gasification under mild gasification conditions (250 °C for 90 min). Total organic carbon, carbohydrate content, and lignin breakdown components of the biomass feeds were compared and linked to the overall gasification performance and hydrogen production yield for the forest-based biomass studied. The gasification of the biomass hydrolysates in the presence of a carbon-supported 10% Pt catalyst resulted in gas mixtures that were composed of more than 90% hydrogen. It was observed that mostly carbohydrate-derived compounds were consumed to produce gaseous products, while lignin-derived compounds in the biomass hydrolysates were not very reactive in the hydrothermal gasification reactions. Softwoods (eastern hemlock, spruce, and loblolly pine) produced more gaseous products than hardwoods studied (black walnut and soft maple). The average of the total gas mixture produced from hardwoods (385 ± 15 mL) was lower than that of softwoods (543 ± 25 mL). The gasification performance of invasive biomass, Japanese honeysuckle, and sumac were the same as softwoods, while autumn olive was between softwoods and hardwoods.","PeriodicalId":25,"journal":{"name":"ACS Sustainable Chemistry & Engineering","volume":"218 1","pages":""},"PeriodicalIF":8.4,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143819751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study explores the transformative role of artificial intelligence (AI) and machine learning (ML) in materials science, leveraging large language models (LLMs) such as OpenAI’s ChatGPT. Focusing on (oxy)hydroxides as oxygen evolution reaction (OER) catalysts, we demonstrate how LLMs streamline data extraction, significantly reducing reliance on traditional, time-intensive methods. Using few-shot training and strategic prompting, ChatGPT achieved an extraction accuracy of approximately 0.9. The curated data set was then used to predict OER performance via the PyCaret library to evaluate various ML algorithms and a high-accuracy XGBoost regression model with accuracies above 0.9 is subsequently established. Further analysis using SHAP and Python Symbolic Regression (PySR) identified key descriptors-electrochemical double-layer capacitance, transition metal composition, support material, and d-electron count-as critical factors, consistent with established electrochemical principles. Additionally, SHAP’s extreme values for Cu and Zn suggest unconventional catalytic roles, potentially linked to Cu2O-facilitated NiOOH formation and Zn-induced electronic modulation, demonstrating the power of data-driven analysis in uncovering hidden mechanisms. To enhance literature-based insights, Microsoft’s GraphRAG technology was employed for in-depth chemical information retrieval. Overall, this study introduces an innovative, end-to-end ML framework powered by ChatGPT, promoting broader AI adoption in scientific research and bridging computational intelligence with experimental sciences.
{"title":"Large Language Models Assisted Materials Development: Case of Predictive Analytics for Oxygen Evolution Reaction Catalysts of (Oxy)hydroxides","authors":"Chenyang Wei, Yutong Shi, Wenbo Mu, Hongyuan Zhang, Rui Qin, Yijun Yin, Gangqiang Yu, Tiancheng Mu","doi":"10.1021/acssuschemeng.5c00798","DOIUrl":"https://doi.org/10.1021/acssuschemeng.5c00798","url":null,"abstract":"This study explores the transformative role of artificial intelligence (AI) and machine learning (ML) in materials science, leveraging large language models (LLMs) such as OpenAI’s ChatGPT. Focusing on (oxy)hydroxides as oxygen evolution reaction (OER) catalysts, we demonstrate how LLMs streamline data extraction, significantly reducing reliance on traditional, time-intensive methods. Using few-shot training and strategic prompting, ChatGPT achieved an extraction accuracy of approximately 0.9. The curated data set was then used to predict OER performance via the PyCaret library to evaluate various ML algorithms and a high-accuracy XGBoost regression model with accuracies above 0.9 is subsequently established. Further analysis using SHAP and Python Symbolic Regression (PySR) identified key descriptors-electrochemical double-layer capacitance, transition metal composition, support material, and d-electron count-as critical factors, consistent with established electrochemical principles. Additionally, SHAP’s extreme values for Cu and Zn suggest unconventional catalytic roles, potentially linked to Cu<sub>2</sub>O-facilitated NiOOH formation and Zn-induced electronic modulation, demonstrating the power of data-driven analysis in uncovering hidden mechanisms. To enhance literature-based insights, Microsoft’s GraphRAG technology was employed for in-depth chemical information retrieval. Overall, this study introduces an innovative, end-to-end ML framework powered by ChatGPT, promoting broader AI adoption in scientific research and bridging computational intelligence with experimental sciences.","PeriodicalId":25,"journal":{"name":"ACS Sustainable Chemistry & Engineering","volume":"80 1","pages":""},"PeriodicalIF":8.4,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143775782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study explores the transformative role of artificial intelligence (AI) and machine learning (ML) in materials science, leveraging large language models (LLMs) such as OpenAI’s ChatGPT. Focusing on (oxy)hydroxides as oxygen evolution reaction (OER) catalysts, we demonstrate how LLMs streamline data extraction, significantly reducing reliance on traditional, time-intensive methods. Using few-shot training and strategic prompting, ChatGPT achieved an extraction accuracy of approximately 0.9. The curated data set was then used to predict OER performance via the PyCaret library to evaluate various ML algorithms and a high-accuracy XGBoost regression model with accuracies above 0.9 is subsequently established. Further analysis using SHAP and Python Symbolic Regression (PySR) identified key descriptors-electrochemical double-layer capacitance, transition metal composition, support material, and d-electron count-as critical factors, consistent with established electrochemical principles. Additionally, SHAP’s extreme values for Cu and Zn suggest unconventional catalytic roles, potentially linked to Cu2O-facilitated NiOOH formation and Zn-induced electronic modulation, demonstrating the power of data-driven analysis in uncovering hidden mechanisms. To enhance literature-based insights, Microsoft’s GraphRAG technology was employed for in-depth chemical information retrieval. Overall, this study introduces an innovative, end-to-end ML framework powered by ChatGPT, promoting broader AI adoption in scientific research and bridging computational intelligence with experimental sciences.
本研究利用 OpenAI 的 ChatGPT 等大型语言模型 (LLM)，探索人工智能 (AI) 和机器学习 (ML) 在材料科学中的变革性作用。以作为氧进化反应（OER）催化剂的（氧）氢氧化物为重点，我们展示了 LLM 如何简化数据提取，大大减少对传统的时间密集型方法的依赖。通过少量训练和策略性提示，ChatGPT 实现了约 0.9 的提取准确率。随后，通过 PyCaret 库，将策划好的数据集用于预测 OER 性能，以评估各种 ML 算法，随后建立了一个准确率高于 0.9 的高精度 XGBoost 回归模型。使用 SHAP 和 Python 符号回归 (PySR) 进行的进一步分析确定了关键描述符--电化学双层电容、过渡金属成分、支持材料和 d 电子计数--是关键因素，这与既定的电化学原理是一致的。此外，SHAP 中 Cu 和 Zn 的极值表明，它们具有非常规的催化作用，可能与 Cu2O 促进 NiOOH 形成和 Zn 诱导的电子调制有关，这证明了数据驱动分析在揭示隐藏机制方面的威力。为了增强基于文献的洞察力，我们采用了微软的 GraphRAG 技术来进行深入的化学信息检索。总之，这项研究引入了一个由 ChatGPT 支持的创新型端到端 ML 框架，促进了人工智能在科学研究中的广泛应用，并将计算智能与实验科学连接起来。
{"title":"Large Language Models Assisted Materials Development: Case of Predictive Analytics for Oxygen Evolution Reaction Catalysts of (Oxy)hydroxides","authors":"Chenyang Wei, Yutong Shi, Wenbo Mu*, Hongyuan Zhang, Rui Qin, Yijun Yin, Gangqiang Yu* and Tiancheng Mu*, ","doi":"10.1021/acssuschemeng.5c0079810.1021/acssuschemeng.5c00798","DOIUrl":"https://doi.org/10.1021/acssuschemeng.5c00798https://doi.org/10.1021/acssuschemeng.5c00798","url":null,"abstract":"<p >This study explores the transformative role of artificial intelligence (AI) and machine learning (ML) in materials science, leveraging large language models (LLMs) such as OpenAI’s ChatGPT. Focusing on (oxy)hydroxides as oxygen evolution reaction (OER) catalysts, we demonstrate how LLMs streamline data extraction, significantly reducing reliance on traditional, time-intensive methods. Using few-shot training and strategic prompting, ChatGPT achieved an extraction accuracy of approximately 0.9. The curated data set was then used to predict OER performance via the PyCaret library to evaluate various ML algorithms and a high-accuracy XGBoost regression model with accuracies above 0.9 is subsequently established. Further analysis using SHAP and Python Symbolic Regression (PySR) identified key descriptors-electrochemical double-layer capacitance, transition metal composition, support material, and d-electron count-as critical factors, consistent with established electrochemical principles. Additionally, SHAP’s extreme values for Cu and Zn suggest unconventional catalytic roles, potentially linked to Cu<sub>2</sub>O-facilitated NiOOH formation and Zn-induced electronic modulation, demonstrating the power of data-driven analysis in uncovering hidden mechanisms. To enhance literature-based insights, Microsoft’s GraphRAG technology was employed for in-depth chemical information retrieval. Overall, this study introduces an innovative, end-to-end ML framework powered by ChatGPT, promoting broader AI adoption in scientific research and bridging computational intelligence with experimental sciences.</p>","PeriodicalId":25,"journal":{"name":"ACS Sustainable Chemistry & Engineering","volume":"13 14","pages":"5368–5380 5368–5380"},"PeriodicalIF":7.1,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143825040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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