M. Olga Guerrero-Pérez

Temperature‐programmed desorption (TPD) characterizes the surface active sites of functional materials, such as adsorbents (metal hydrides, porous carbons, zeolites, metal–organic frameworks, for example) and catalysts. Solids surfaces are pre‐treated to remove moisture, contaminants, and previously adsorbed species, then a probe molecule (, , , ) saturates the surface. After an inert purge, a furnace ramps the temperature and a mass‐spectrometer (MS) or thermal conductivity detector (TCD) monitors the effluent. We deduce the binding energy of the probe molecule, the surface strength, and kinetic mechanism based on the shape and position of the concentration peaks generated by the MS or TCD. The Redhead equation estimates desorption energies for systems with simple kinetics, negligible re‐adsorption, and coverage‐independent activation energy. The Polanyi–Wigner equation is more robust and relates the desorption rate to surface coverage, desorption barrier, desorption order, and temperature. Among the 50 of the 250 scientific disciplines in Web of Science Core Collection physical chemists apply the technique most followed by chemical engineers. The five themes of research are centred on: (1) zeolites, hydrogenation, and methanol; (2) oxidation, nanoparticles, and reduction; (3) adsorption, decomposition, and kinetics; (4) , , and Ni; and (5) catalyst, biodiesel, and transesterification.

This work introduced an interpretable machine learning framework for the predictive modeling of CO2 hydrogenation performance for methanol production using the Extra Trees Regression, Histogram Gradient Boosting, and CatBoost algorithms, in terms of CO2 conversion efficiency, methanol selectivity, and CO selectivity. Among the approaches evaluated, Extra Trees demonstrated superior performance for predicting methanol and CO selectivity, whereas Histogram-based Gradient Boosting regression exhibited slightly better generalization for CO2 conversion efficiency. Furthermore, SHAP analysis showed that temperature was the most critical parameter with the highest absolute mean SHAP values for all targets and high nonlinearity. Dependence plots also showed a positive thermal promotion of CO2 conversion and CO selectivity, with a decrease in methanol selectivity at higher temperatures. More importantly, Monte Carlo simulations were performed to quantify predictive uncertainty, and the uncertainty bands spanning 5 to 95% of the model predictions were found to be 15.1%, 41.5%, and 45.9% for conversion, methanol selectivity, and CO selectivity, respectively. Hold-out validation of the models was confirmed by residuals randomly distributed around a mean of zero. Generally, the integrated modeling and explainability framework provides a reliable, transparent, and uncertainty-aware method for accelerating catalyst optimization and the operational strategy development in CO2 valorization systems.

This work presented an experimentally validated machine learning framework to predict and interpret the properties of producer gas obtained through steam-aided Eucalyptus wood gasification in a downdraft fixed-bed gasifier. The systematic experimental data were collected by varying the equivalence ratio (ER) and biomass consumption rate (BCR). Under optimum conditions, the process recorded a cold gas efficiency (CGE) of up to 82%, a lower heating value (LHV) of 6.8 MJ/kg, and a peak hydrogen concentration of 22.29%, proving that steam integration was effective in improving the quality of the producer gas. Random forest (RF)- and extreme gradient boosting (XGBoost)-based machine learning models were developed as surrogate predictors across the three targets (CGE, LHV, and hydrogen yield). As a result, RF attained training R2 values of 0.990–0.994, while XGBoost achieved ranges of 0.953–0.973. In addition, SHAP-based explainable artificial intelligence was employed to provide interpretable insights into feature significance, indicating that ER is a strong determinant of hydrogen production and CGE, whereas BCR is the primary determinant of LHV. More importantly, the combined surrogate-based Sobol sensitivity analysis and Monte Carlo uncertainty quantification demonstrated rapid convergence within 200–400 samples, confirming the predominant dominance of BCR on LHV (Sobol index of 0.95–0.98) and ER on the hydrogen yield (0.98). Overall, the presented framework was more progressive than traditional modeling approaches because it embedded the crucial elements of interpretability and uncertainty quantification, allowing operators to optimize steam-assisted gasification units with high confidence rates.

Magnetometry is a non‐invasive technique to characterize the behaviour of magnetic materials of which the most common contain Fe, Co, and Ni. Vibrating sample magnetometers (VSMs) vibrate samples at a fixed frequency in a static, homogeneous magnetic field to induce a voltage in pick up coils according to Faraday's law of induction. This voltage is proportional to the magnetic moment of the sample, allowing magnetization to be studied. Magnetometers monitor active phases and particle size distribution changes of catalysts operando . Despite the huge potential for this technique in chemical engineering, it remains on the edge and is slowly becoming more popular with 80 articles a year indexed by Web of Science Core Collection™. It remains mostly in the realm of physics and method development: cantilever magnetometry, vector magnetometry, torque magentometry, SQUID magnetometry (superconduction quantum devices), atomic magnetometry, and magentoencephalography. The complimentary techniques in chemical engineering are nuclear magentic resonance (NMR), Mössbauer spectroscopy, atomic force microscopy (AFM), and elecron paramagnetic resonance/electron spin resonance (EPR/ESR). The main limitation is that only strong ferro or ferrimagnetic samples can be studied, while the weaker paramagnetic or antiferromagnetic phases are obscured by the background contribution, due to the lack of sensitivity. Magnetometry is a bulk technique that measures only the overall magnetization, and subsequently it is problematic to identify contributions from materials comprising multiple phases. Other limitations are related to the effect of chemisorption on the magnetization, thermal blocking and domain structure that complicates determining the particle size distribution.

Since many decades ago, it is known that V oxides may be conductive, in addition to other interesting properties, as the ability to insert/extract cations and to form mixed valence oxides of different colors, which make them useful for the fabrication of devices in which electrons and cation transport are required, as well as batteries, supercapacitors, sensors, and electrochromic devices. This contribution documents, from a historical perspective, the advancements that have been made in the use of V‐oxide‐based materials in these devices with a detailed discussion on their synthesis methods, phases formed, and analysis of the applications to offer a coherent synthesis on the relationships between the structure and the behavior of the V‐based devices for energy applications.