Fahimeh Hooriabad Saboor

Asphaltene precipitation and deposition remain major flow assurance challenges, yet solid adsorbents are often reported without sufficient links between uptake, model discrimination, uncertainty, and mechanism. Here we present green hydrothermally synthesized MIL-100(Fe) as a reusable nanoadsorbent for asphaltene removal from toluene model oil, supported by characterization, adsorption experiments, and a fully traceable data-analysis package. The material exhibited a BET surface area of 1950 m2 g−1, a pore volume of 1.63 cm3 g−1, and an average pore diameter of 3.3 nm. Adsorption approached equilibrium within about 60 min, reached 179.7 mg g−1 at the highest tested concentration, and achieved 83.2% removal under the low-concentration optimum. MIL-100(Fe) retained 72.1% removal after four cycles. Nonlinear isotherm fitting, information-criterion ranking, and bootstrap analysis supported Langmuir-type uptake at 298–318 K, while kinetic diagnostics indicated rapid pseudo-second-order behaviour with additional heterogeneous-surface contributions. Thermodynamic and molecular weight sensitivity analyses confirmed spontaneous, exothermic adsorption. FTIR shifts support pore filling, aromatic interactions, hydrogen bonding, and contacts with FeO/coordination environments. This work positions MIL-100(Fe) as a high-capacity, regenerable, and mechanistically auditable adsorbent for asphaltene mitigation.

Machine-learning-guided MIL-100(Fe) synthesis reveals that methylene blue removal is maximized by application-specific functionality, not by BET surface area alone.

Traditional MOF design often maximizes generic metrics such as BET surface area and crystallinity, assuming they universally predict performance. We present a machine-learning-guided optimization framework for MIL-100(Fe) from experimentally synthesized samples. All materials were synthesized via an acid-free, water-based hydrothermal route. We trained small-data ML models to link synthesis parameters, including temperature, time, metal-to-ligand ratio, and ion concentration, to key properties comprising surface area, total pore volume, average crystallite size, crystallinity, yield, and methylene blue (MB) removal. SHAP analysis showed that time and metal-to-ligand ratio dominated dye removal, whereas surface area was more sensitive to temperature and time. The most accurate model, gaussian process regression was coupled with a genetic algorithm (GA) to optimize synthesis for property-specific targets. Through optimization, we achieved a 5.37% rise in BET surface area (1748\ to 1841.9 m²/g) and a 12.1% gain in MB removal (88.6 to 98.3%) over the dataset’s best results. The MB removal optimized sample with a surface area of 1274.3 m²/g and 17.4% crystallinity, delivers the highest MB uptake of 98.3%. While, optimized sample for surface area, reaches 1841.9 m²/g (about 44% higher) with 34.9% crystallinity but achieves only 85.1% removal, about 13% lower than optimized MB sample; indicating that even substantial increases in surface area do not govern adsorption performance. A t-SNE analysis further revealed distinct clusters in property space, with optimized samples occupying separate neighborhoods, elucidating that MOF synthesis should be tailored to the target application rather than a single metric such as surface area.

We studied the performance of fine-tuning open-source LLMs for a range of different chemical questions. We benchmark their performances against “traditional” machine learning models and find that, in most cases, the fine-tuning approach is superior.

This study investigates the effect of solvent type on the structural properties and adsorption performance of the ZIF-8 metal-organic framework for removing various dyes including, methyl green (MG), methylene blue (MB), and methyl orange (MO) from water in acidic and alkaline environments. ZIF-8 samples were synthesized using zinc nitrate, methylimidazole, and three different solvents including, water, methanol, and ethanol under atmospheric pressure and 70 °C. Characterization using BET, XRD, FT-IR, and TGA techniques sheds light on the structural, chemical, and thermal properties of ZIF-8 samples. Among the samples, ZIF-8/M, synthesized using methanol, stands out, demonstrating the high surface area of 2172.7 m2/g, large total pore volume of 1.5412 cm3/g, and high crystallinity of 31.9% with improved thermal stability. Furthermore, ZIF-8/M shows better adsorption performance for methyl green with a removal percentage of 81.9%, 87.1%, and an adsorption capacity of 20.5 mg/g and 21.8 mg/g, in acidic and alkaline environments, respectively. Enhanced dye adsorption of ZIF-8/M is associated with both physical and effective chemical adsorption mechanisms via tuning the environment's acidity.

The current generation of large language models (LLMs), like ChatGPT, have limited chemical knowledge. Recently, it has been shown that these LLMs can learn and predict chemical properties through fine-tuning. In this work, we explore the potential and limitations of this approach. We studied the performance of fine-tuning GPT-J-6B, a public-domain version of the GPT family, for a range of different chemical questions. We find that in most, if not all, cases, this approach outperforms the benchmark (random guessing) for a simple classification problem. Depending on the size of the dataset and the type of questions, we can also address more sophisticated problems. The most important conclusions of this work are that, for all datasets considered, their conversion into an LLM fine-tuning training set is straightforward and that fine-tuning with even relatively small datasets leads to predictive models. These results suggest that the systematic use of LLMs to guide experiments and simulations will be a powerful technique in any research study, significantly reducing unnecessary experiments or computations.

The application of organic-inorganic nanocomposite membranes allows for a synergy between the desirable thermal and mechanical properties of inorganic materials with the reactivity, dielectric properties, durability, flexibility, and processability of the polymeric materials. Proton exchange membrane fuel cells (PEMFCs) suffer from some problems including water content management, carbon monoxide poisoning, hydrogen reformate, and fuel crossover through the membrane. Herein, specific solutions have been proposed to the above-mentioned problems using organ-inorganic nanocomposites. These solutions include doping proton conductive inorganic nano-particles in the proton exchange membrane, preparing nanocomposites via the sol-gel method, covalence bond of inorganic compounds with the polymer structure, and acid-based proton exchange nanocomposite membranes. Furthermore, hydrogen production with low carbon monoxide content using the ethanol steam reforming method, as well as the effect of CO in the hydrogen feed of PEMFC are explained and discussed. Finally, desirable conditions for achieving the maximum power density in exchange membrane cells (EMFCs) are discussed.