Holtzapple Advances Commercialization of Carboxylic Platform
Since 1991, Dr. Mark Holtzapple’s research group has been developing the carboxylate platform, a process that converts biomass to carboxylic acids (e.g., acetic, propionic, butyric acids). These acids are valuable chemicals, and also can be used as intermediates to produce industrial chemicals and fuels.
Texas A&M Energy Institute Announces 2022-2023 Lecture Series
The Texas A&M Energy Institute is proud to announce its 2022-2023 Lecture Series schedule, which includes prominent energy leaders from across many energy disciplines and topics. Running from September 2022 through May 2023, this cohort of experts will present the latest innovations, concepts, and topics and will be sure to spark thoughts and discussions as […]
Boosting agility, resilience of supply chains through automation and robotics
The COVID-19 pandemic exposed the inability of national supply chains to quickly shift production and reconfigure their logistics networks to meet customer demand surges during major disruptive events. The desperate scramble for items such as ventilators, face masks and even toilet paper won’t soon be forgotten, but the recent baby formula shortage points to a […]
Pistikopoulos Named Association of Former Students Distinguished Achievement Award Recipient
Texas A&M University and The Association of Former Students have selected 24 outstanding faculty and staff to be honored with the 2022 Distinguished Achievement Awards. Since 1955 the Distinguished Achievement Awards have been awarded to those who exhibit the highest standards of excellence at Texas A&M. Stratos Pistikopoulos, the director of The Texas A&M Energy Institute, has been named a 2022 recipient of one of the Research awards.
Texas A&M Researchers Aim To Accurately Monitor Subsurface Carbon Dioxide Storage
Their novel monitoring system can rapidly monitor carbon dioxide sequestered underground. Capturing and storing carbon dioxide (CO2) deep underground can help combat climate change, but long-term monitoring of the stored CO2 within a geological storage site is difficult using current physics-based methods. Texas A&M University researchers proved that unsupervised machine-learning methods could analyze the sensor-gathered […]