Accelerated discovery of polymer donors for organic solar cells through machine learning: From library creation to performance forecasting.

The design of novel polymer donors for organic solar cells has been a major research focus for decades, but discovering unique materials remains challenging due to the high cost of experimentation. In this study, machine learning models are employed to predict power conversion efficiency (PCE), Mordred descriptors are used for model training. Among the four machine learning models evaluated, the gradient boosting regressor emerged as the best-performing model. Additionally, a chemical library of polymer donors was generated and analyzed using various measures. 30 donors with highest PCE are selected and their synthetic accessibility is evaluated. Similarity analysis has indicated much resemblance in selected polymer donors.

Synergistic Interaction during Copyrolysis of Soybean Straw and Tire Waste: Improving Emissions and Product Quality.

This study explores copyrolysis of soybean straw (SS) with hydrogen-rich tire waste (TW) to enhance pyrolytic product quality and reduce pollutant emissions. Addition of TW increased SS biomass conversion from 67.19 to 72.46% and decreased coke/residue formation from 32.81 to 27.54%. The activation energy dropped to 121.84 kJ/mol from 160.73 kJ/mol (as calculated by the Kissinger-Akahira-Sunose method) and 122.78 kJ/mol from 159.76 kJ/mol (as calculated by the Ozawa-Flynn-Wall method). Thermogravimetric analysis coupled with Fourier-transform infrared spectroscopy (TG-FTIR) showed lowered CO2, NO2, and SO2 emissions (5.58, 5.72, 3.38) compared to conventional SS pyrolysis (18.38, 11.55, 12.37). Yields of value-added chemicals (phenols, olefins, aromatics) increased (32.38, 22.17, 30.18%) versus conventional SS pyrolysis (23.56, 13.78, 20.36%). Pyrolysis gas chromatography-mass spectrometry (Py/GC-MS) analysis reveals that the addition of TW leads to a decrease in the production of oxygenates and polycyclic aromatic hydrocarbons, reducing their yields to 8.96 and 7.67%, respectively, down from 19.37 and 14.37%. Simultaneously, it enhances the yields of olefins, aromatics, phenols, and aliphatic hydrocarbons to 23.38, 26.78, 26.17, and 25.78%, respectively, compared to 15.37%, 15.29, 18.36, and 17.25%, respectively, in the absence of TW. In summary, copyrolysis of TW with SS improves product quality and reduces pollutant emissions, marking a significant research contribution.

Machine learning assisted designing of conjugated organic chromophores, light absorption, and emission behavior prediction.

Organic materials have several important characteristics that make them suitable for use in optoelectronics and optical signal processing applications. For absorption and emission maxima, the stabilities and photoactivities of conjugated organic chromophores can be tailored by selecting a suitable parent structure and incorporating substituents that predictably change the optical characteristics. However, a high-throughput design of efficient conjugated organic chromophores without using trial-and-error experimental approaches is required. In this study, machine learning (ML) is used to design and test the conjugated organic chromophores and predict light absorption and emission behavior. Many machine learning models are tried to select the best models for the prediction of absorption and emission maxima. Extreme gradient boosting regressor has appeared as the best model for the prediction of absorption maxima. Random forest regressor stands out as the best model for the prediction of emission maxima. Breaking Retrosynthetically Interesting Chemical Substructures (BRICS) is used to generate 10,000 organic chromophores. Chemical similarity analysis is performed to obtain a deeper understanding of the characteristics and actions of compounds. Furthermore, clustering and heatmap approaches are utilized.

Data mining and library generation to search electron-rich and electron-deficient building blocks for the designing of polymers for photoacoustic imaging.

Photoacoustic imaging is a good method for biological imaging, for this purpose, materials with strong near infrared (NIR) absorbance are required. In the present study, machine learning models are used to predict the light absorption behavior of polymers. Molecular descriptors are utilized to train a variety of machine learning models. Building blocks are searched from chemical databases, as well as new building blocks are designed using chemical library enumeration method. The Breaking Retrosynthetically Interesting Chemical Substructures (BRICS) method is employed for the creation of 10,000 novel polymers. These polymers are designed based on the input of searched and selected building blocks. To enhance the process, the optimal machine learning model is utilized to predict the UV/visible absorption maxima of the newly designed polymers. Concurrently, chemical similarity analysis is also performed on the selected polymers, and synthetic accessibility of selected polymers is calculated. In summary, the polymers are all easy to synthesize, increasing their potential for practical applications.

Ecological risk assessment of heavy metals in bottom ashes generated by small-scale thermal treatment furnaces for domestic waste in villages and towns of China.

Small-scale Solid Waste Thermal Treatment (SSWTT) is prevalent in remote Chinese locations. However, the ecological threats associated with heavy metals in resultant bottom ash remain undefined. This research study scrutinized such ash from eight differing sites, assessing heavy metal content, chemical form, and leaching toxicity. Most bottom ash samples met soil contamination standards for development land (GB36600-2018). However, levels of As, Cd, Cr, Cu, Ni, Pb, and Zn in some samples exceeded agricultural land standards GB15618-2018) by 1591%, 64,478%, 1880%, 3886%, 963%, 1110%, and 2011% respectively. Additionally, the As and Cd contents surpassed the construction land control limit value by 383% and 13% respectively. The mean values of the combined oxidizable and residual fraction (F3 + F4) for each heavy metal in all samples exceeded 65%, with Cr, Cu, Ni, and Pb reaching over 95%. All sample leaching concentrations, obtained via the HJ/T 299 procedure, were less than limits set by the identification standards for hazardous wastes (GB5085.3-2007). However, only the leaching concentrations of three samples via the leaching procedure HJ/T 300 met the "Solid Waste Landfill Pollution Control Standard" (GB 16889-2008). The results indicate that the location and type of SSWTT equipment play a crucial role in determining an appropriate solution for bottom ash management.

Performance prediction of polymer-fullerene organic solar cells and data mining-assisted designing of new polymers.

CONTEXT: Selecting high performance polymer materials for organic solar cells (OSCs) remains a compelling goal to improve device morphology, stability, and efficiency. To achieve these goals, machine learning has been reported as a powerful set of algorithms/techniques to solve complex problems and help/guide exploratory researchers to screen, map, and develop high performance materials. In present work, we have applied machine learning tools to screen data from reported studies and designed new polymer acceptor materials, respectively. Quantitative structure-activity relationship (QSAR) models were generated using machine learning-assisted simulation techniques. For this purpose, 3000 molecular descriptors are generated. Consequently, molecular descriptors having key effect on power conversion efficiency (PCE) were identified. Moreover, numerous regression models (e.g., random forest and bagging regressor models) were developed to predict the PCE. In particular, new materials were designed based on the similarity analysis. The GDB17 chemical database consisting of 166 million organic molecules in an ordered form is used for performing similarity analysis. A similarity behavior between GDB17 materials and the materials reported in literature is studied using RDKit (a cheminformatics software). Noteworthily, 100 monomers proved to be unique and effective, and PCEs of these monomers are predicted. Among these monomers, four monomers exhibited PCE higher than 14%, which is better than various reported studies. Our methodology provides a unique, time- and cost-efficient approach to screening and designing new polymers for OSCs using similarity analysis without revisiting the reported studies. METHODS: To perform machine learning analysis, data from reported studies and online databases was collected. Different molecular descriptors were generated for polymer materials utilizing Dragon software. 3D structures of studied molecules were applied as input (SDF; structure data file format). Importantly, about 3000 molecular descriptors were generated. Comma-separated value (.csv) file format was used to export these molecular descriptors. To shortlist best descriptors, univariate regression analysis was performed. These descriptors were further utilized for training machine learning models. Moreover, necessary packages of Python for data analysis and visualization were imported such as Matplotlib, Numpy, Pandas, Scikit-learn, Seaborn, and Scipy. Random forest and bagging regressor models were applied for performing machine learning analysis. A cheminformatics software, RDKit, was applied for similarity analysis.

Pyrolysis characteristics, kinetics, and biochar of fermented pine sawdust-based waste.

The objective of current study is to explore the energy recovery potential of fermentation residues. In this perspective, pyrolysis characteristics, kinetics, and modified biochar derived from pine sawdust after fermentation (FPD) were determined, and comparison was established with pine sawdust (PD). The variation range of comprehensive pyrolysis index (CPI) values of FPD was found 6.51 × 10-7-16.38 × 10-7%2·min-2·°C-3, significantly higher than that of untreated samples determined under the same experimental conditions. The average activation energy of FPD was 367.95 kJ/mol, 389.45 kJ/mol, and 346.55 kJ/mol calculated by Flynn-Wall-Ozawa (FWO) method, Kissinger-Akahira-Sonuse (KAS), and Starink method respectively, and importantly, these values are much higher than those of PD. Additionally, fermentation could enhance the adsorption capacity for methylene blue of biochar from 0.76 mg/g to 1.6 mg/g due to the abundant surface functional groups and three-dimensional internal pore structure. The adsorption pattern of fermented pine wood shifted from chemisorption dominated to the synergetic adsorption of surface functional groups adsorption and intragranular filling. These results show that FPD has favorable pyrolytic properties, and the derived biochar has adsorption properties, which is the basis for designing pyrolysis process and reusing fermentation residues. HIGHLIGHTS: The FPD has higher values of CPI and activation energy than the PD. FPD-derived biochar has higher adsorption capacity than PD-derived biochar. The fermentation improves the pyrolysis performance. The fermentation enhances adsorption capacity due to unique structure of biochar.

Enzyme-Assisted Extraction of Phenolics from Capparis spinosa Fruit: Modeling and Optimization of the Process by RSM and ANN.

The current study intends to appraise the effect of enzyme complexes on the recovery of phenolics from Capparis spinosa fruit extract using the response surface methodology (RSM) and artificial neural networking (ANN). Enzymatic treatment of C. spinosa fruit extract was optimized under a set of conditions (enzyme concentration, pH, temperature, and time) against each enzyme formulation such as Kemzyme Plus Dry, Natuzyme, and Zympex-014. The extract yield observed for Kemzyme Plus Dry (42.00%) was noted to be higher than those for Zympex-014 (39.80%) and Natuzyme (38.50%). Based on the higher results, the values of Kemzyme Plus Dry-based extract were further employed in different parameters of RSM. The F-value (16.03) and p-values (<0.05) implied that the selected model is significant. Similarly, the higher values for the coefficient of determination (R 2) at 0.9740 and adjusted R 2 (adj. R 2) at 0.9132 indicated that the model is significant in relation to given experimental parameters. ANN-predicted values were very close to the experimental values, which demonstrated the applicability of the ANN model. Antioxidant activities also exhibited profound results in terms of total phenolic content values (24.76 mg GAE/g), total flavonoid content values (24.56 mg CE/g), and the 2,2-diphenyl-1-picrylhydrazyl (DPPH) assay (IC50) (5.12 mg/mL). Scanning electron microscopy revealed that after enzymatic hydrolysis, the cell walls were broken as compared with nonhydrolyzed materials. Five phenolics, namely, quercetin, m-coumaric acid, sinapic acid, kaempferol, and p-coumaric acid, were identified from C. spinosa extract by gas chromatography-mass spectrometry (GC/MS). The results of this study reveal that the proposed optimization techniques, using Kemzyme Plus Dry among others, had a positive effect on the recovery of phenolic bioactive compounds and thus increased the antioxidant potential of C. spinosa fruit extract.

Biological and Hypoglycemic Effects of a Polyherbal Extract on Alloxanized Diabetic Rats.

The current study investigates the antioxidant, antidiabetic, hepatoprotective, and nephroprotective potentials of a polyherbal mixture containing the methanolic extracts of seeds from Nigella sativa, Cicer arietinum, Silybum marianum, and Citrullus colocynthis and the rhizome of Zingiber officinale. The polyherbal extract (PHE) showed significant total phenolic contents (187.17 GAE/g), ferric reducing power (28%), and radical-scavenging activity (86.16%). The PHE also showed a substantial hypoglycemic effect in alloxan-induced diabetic rats by reducing the blood glucose level of the PHE-treated rats (-48.64%) and increasing the insulin level (107.5%) as compared with the diabetic control group. Likewise, an increase in high-density lipoprotein (HDL) contents (22.95%) with an associated decrease in low-density lipoprotein (LDL) levels (-43.93%) was also noted. A significant decrease in serum levels of liver marker enzymes, e.g., SGPT (-36%), SGOT (-31%), and serum ALP (-12%), was also observed as compared with the standard drug-treated group. Based on the findings of the study, it may be suggested that PHE helps ameliorate the severity of diabetes as a herbal remedy and might be employed in nutra-pharmaceuticals, replacing synthetic antidiabetic compounds.

Full recycling of high-value resources from cabbage waste by multi-stage utilization.

Cabbage waste (CW) was recycled for generating some potential high-value products by a multi-stage treatment technology. A novel multi-stage utilization process was successfully proposed which consisted of low-temperature extraction, medium-temperature thermolysis, and high-temperature activation. Plant extracts that contain fatty acids, alcohol, furan, and esters were first extracted from raw cabbage waste by ethanol at 70 °C. Pyrolytic oil was obtained by cabbage waste pyrolysis at different medium temperature conditions. The produced carbon residue was further activated at high temperature for environmental purification such as VOCs removal. The performance of this process was characterized by N2 isothermal adsorption, Fourier transform infrared spectrometer (FTIR), thermogravimetric analysis (TG) and gas chromatography-mass spectrometry (GC-MS). Experimental results showed that the optimum temperatures for extraction, pyrolysis, and activation were 70 °C, 520 °C and 700 °C, respectively. Phenolic-rich pyrolysis solution with 50% phenolic contents could be obtained with the potential application of botanical pesticide. The produced biochar had a BET surface area of as high as 891.12 m2/g. The yields of biochar, pyrolytic liquid, and pyrolytic gas were 43.86%, 17.47%, 38.67%, respectively, and the process energy efficiency was over 42.7%. Applicability and feasibility of this process were also discussed in the aspects of energy quality balance, economy, and environment. The proposed multi-stage thermal-chemical process could be used as a full recycling method for biomass waste.

Catalytic Fast Pyrolysis of Soybean Straw Biomass for Glycolaldehyde-Rich Bio-oil Production and Subsequent Extraction.

In this study, soybean straw (SS) as a promising source of glycolaldehyde-rich bio-oil production and extraction was investigated. Proximate and ultimate analysis of SS was performed to examine the feasibility and suitability of SS for thermochemical conversion design. The effect of the co-catalyst (CaCl2 + ash) on glycolaldehyde concentration (%) was examined. Thermogravimetric-Fourier-transform infrared (TG-FTIR) analysis was applied to optimize the pyrolysis temperature and biomass-to-catalyst ratio for glycolaldehyde-rich bio-oil production. By TG-FTIR analysis, the highest glycolaldehyde concentration of 8.57% was obtained at 500 °C without the catalyst, while 12.76 and 13.56% were obtained with the catalyst at 500 °C for a 1:6 ratio of SS-to-CaCl2 and a 1:4 ratio of SS-to-ash, respectively. Meanwhile, the highest glycolaldehyde concentrations (%) determined by gas chromatography-mass spectrometry (GC-MS) analysis for bio-oils produced at 500 °C (without the catalyst), a 1:6 ratio of SS-to-CaCl2, and a 1:4 ratio of SS-to-ash were found to be 11.3, 17.1, and 16.8%, respectively. These outcomes were fully consistent with the TG-FTIR results. Moreover, the effect of temperature on product distribution was investigated, and the highest bio-oil yield was achieved at 500 °C as 56.1%. This research work aims to develop an environment-friendly extraction technique involving aqueous-based imitation for glycolaldehyde extraction with 23.6% yield. Meanwhile, proton nuclear magnetic resonance (1H NMR) analysis was used to confirm the purity of the extracted glycolaldehyde, which was found as 91%.

Pyrolysis of oil extracted safflower seeds: Product evaluation, kinetic and thermodynamic studies.

In this study, pyrolysis kinetics and thermodynamic parameters of Safflower residues (SR) obtained from oil extraction were investigated by using TG/DSC-FTIR and py-GC/MS. Thermal analysis was performed from ambient temperature to 750 °C under a nitrogen atmosphere. The first-order reaction kinetics model was applied to thermal analysis data to determine apparent kinetic parameters. Activation energy and pre-exponential factor were calculated as 76.60 kJ.mol-1 and 1.89x106 min-1, respectively. The thermodynamic parameters such as the change in Gibb's free energy, the difference in enthalpy and the entropy change were calculated to be 201.36 kJ mol-1, 71.79 kJ mol-1, and -0.196 kJ mol-1, respectively. TG/FTIR analysis revealed that CO2, C6H5OH, and CC functional group as the main pyrolysis gas products. According to Py-GC/MS results of SR, the presence of high energy-containing compounds among the pyrolysis products was proved. All these results show that SR is suitable for pyrolysis to produce biofuel and/or chemicals.

Enhanced photocatalytic H2 production under visible light on composite photocatalyst (CdS/NiSe nanorods) synthesized in aqueous solution.

Cocatalysts play a critical role in the activity and stability of photocatalytic systems. Currently, efficient cocatalysts mainly comprise of expensive noble metals. Herein we report a composite photocatalyst consisting of CdS nanorods (NRs) and noble-metal-free cocatalyst NiSe, which efficiently enhances the hydrogen production activity of CdS NRs under visible light. NiSe was synthesized through a facile aqueous solution method and CdS/NiSe NRs composites were prepared by in situ deposition of NiSe on CdS NRs. This provides increased contact between cocatalyst and photosensitizer leading to enhanced electron transfer at the interface of NiSe and CdS. The current photocatalytic system gave the highest hydrogen evolution rate of 340 µmol h-1 under optimal conditions. The enhanced stability of the system was observed for 30 h of irradiation resulting in 14 mmol of hydrogen evolution. The highest AQY of 12% was observed using the 420 nm monochromatic light. In addition, CdS/NiSe NRs showed significant higher H2 evolution rate than that of 1.0 wt% loaded CdS/Pt NRs proving NiSe as highly efficient cocatalyst. Photoluminescence spectra and the photocurrent response were used to confirm the efficient charge transfer at the interface of NiSe and CdS nanorods. The work presented here demonstrates the successful use of an inexpensive, non-noble-metal cocatalyst for enhanced photocatalytic hydrogen production.

Demonstrating the suitability of canola residue biomass to biofuel conversion via pyrolysis through reaction kinetics, thermodynamics and evolved gas analyses.

The identification of biomasses for pyrolytic conversion to biofuels depends on many factors, including: moisture content, elemental and volatile matter composition, thermo-kinetic parameters, and evolved gases. The present work illustrates how canola residue may be a suitable biofuel feedstock for low-temperature (<450 °C) slow pyrolysis with energetically favorable conversions of up to 70 wt% of volatile matter. Beyond this point, thermo-kinetic parameters and activation energies, which increase from 154.3 to 400 kJ/mol from 65 to 80% conversion, suggest that the energy required to initiate conversion is thermodynamically unfavorable. This is likely due to its higher elemental carbon content than similar residues, leading to enhanced carbonization rather than devolatilization at higher temperatures. Evolved gas analysis supports limiting pyrolysis temperature; ethanol and methane conversions are maximized below 500 °C with ∼6% water content. Carbon dioxide is the dominant evolved gas beyond this temperature.