Vacancy Engineering in 2D Transition Metal Chalcogenide Photocatalyst: Structure Modulation, Function and Synergy Application.

Transition metal chalcogenides (TMCs) are widely used in photocatalytic fields such as hydrogen evolution, nitrogen fixation, and pollutant degradation due to their suitable bandgaps, tunable electronic and optical properties, and strong reducing ability. The unique 2D malleability structure provides a pre-designed platform for customizable structures. The introduction of vacancy engineering makes up for the shortcomings of photocorrosion and limited light response and provides the greatest support for TMCs in terms of kinetics and thermodynamics in photocatalysis. This work reviews the effect of vacancy engineering on photocatalytic performance based on 2D semiconductor TMCs. The characteristics of vacancy introduction strategies are summarized, and the development of photocatalysis of vacancy engineering TMCs materials in energy conversion, degradation, and biological applications is reviewed. The contribution of vacancies in the optical range and charge transfer kinetics is also discussed from the perspective of structure manipulation. Vacancy engineering not only controls and optimizes the structure of the TMCs, but also improves the optical properties, charge transfer, and surface properties. The synergies between TMCs vacancy engineering and atomic doping, other vacancies, and heterojunction composite techniques are discussed in detail, followed by a summary of current trends and potential for expansion.

Distribution characteristics and migration pathways of metals during hydrothermal liquefaction of municipal sewage sludge in the presence of various catalysts.

A series of hydrothermal liquefaction (HTL) experiments with two different samples of municipal sewage sludge (MSS) were conducted at 350 °C for 30 min residence time in a high pressure batch reactor. The main aim of the study was to explore the distribution and migration pathways of a broad range of metals and metalloids in the HTL products (bio-oil, char and aqueous phase) obtained in the presence of various homogeneous and heterogeneous catalysts (Na2CO3, Li2CO3, K2CO3, Ba(OH)2, Fe2O3, CeO2, NiMo/MoO3, MoS2, Ni/NiO, SnO2, FeS). The elements under study included 16 environmentally significant metals and metalloids (As, B, Ba, Cd, Co, Cr, Cu, Mn, Mo, Ni, Pb, Sb, Se, Sn, Zn and Hg). The study showed that the quantitative migration of the tested metals and metalloids to the particular HTL products, relative to their initial content in the raw sludge, is different for the individual elements. Most metals exhibited a particularly strong affinity to the solid fraction (biochar). In the obtained HTL bio-oils, all tested elements were identified, except of Cd. It was also found that B and As have high affinity to the aqueous phase. A direct effect of catalysts on the contents of some elements in the products was also proved by the study, e.g. increased concentration of Cr in the biochar when Fe2O3 was used as a process catalyst. Due to the wide scope of the tested elements and broad range of catalyst used, the results obtained represent a unique and comprehensive set of environmental data compared to similar HTL studies previously conducted for MSS.

Tuning active sites on biochars for remediation of mercury-contaminated soil: A comprehensive review.

Mercury (Hg) contamination is acknowledged as a global issue and has generated concerns globally due to its toxicity and persistence. Tunable surface-active sites (SASs) are one of the key features of efficient BCs for Hg remediation, and detailed documentation of their interactions with metal ions in soil medium is essential to support the applications of functionalized BC for Hg remediation. Although a specific active site exhibits identical behavior during the adsorption process, a systematic documentation of their syntheses and interactions with various metal ions in soil medium is crucial to promote the applications of functionalized biochars in Hg remediation. Hence, we summarized the BC's impact on Hg mobility in soils and discussed the potential mechanisms and role of various SASs of BC for Hg remediation, including oxygen-, nitrogen-, sulfur-, and X (chlorine, bromine, iodine)- functional groups (FGs), surface area, pores and pH. The review also categorized synthesis routes to introduce oxygen, nitrogen, and sulfur to BC surfaces to enhance their Hg adsorptive properties. Last but not the least, the direct mechanisms (e.g., Hg- BC binding) and indirect mechanisms (i.e., BC has a significant impact on the cycling of sulfur and thus the Hg-soil binding) that can be used to explain the adverse effects of BC on plants and microorganisms, as well as other related consequences and risk reduction strategies were highlighted. The future perspective will focus on functional BC for multiple heavy metal remediation and other potential applications; hence, future work should focus on designing intelligent/artificial BC for multiple purposes.

Feature engineering for improved machine-learning-aided studying heavy metal adsorption on biochar.

Due to the broad interest in using biochar from biomass pyrolysis for the adsorption of heavy metals (HMs) in wastewater, machine learning (ML) has recently been adopted by many researchers to predict the adsorption capacity (η) of HMs on biochar. However, previous studies focused mainly on developing different ML algorithms to increase predictive performance, and no study shed light on engineering features to enhance predictive performance and improve model interpretability and generalizability. Here, based on a dataset widely used in previous ML studies, features of biochar were engineered-elemental compositions of biochar were calculated on mole basis-to improve predictive performance, achieving test R2 of 0.997 for the gradient boosting regression (GBR) model. The elemental ratio feature (H-O-2N)/C, representing the H site links to C (non-active site to HMs), was proposed for the first time to help interpret the GBR model. The (H-O-2N)/C and pH of biochar played essential roles in replacing cation exchange capacity (CEC) for predicting η. Moreover, expanding the coverages of variables by adding cases from references improved the generalizability of the model, and further validation using cases without CEC and specific surface area (R2 0.78) and adsorption experimental results (R2 0.72) proved the ML model desirable. Future studies in this area may take into account algorithm innovation, better description of variables, and higher coverage of variables to further increase the model's generalizability.

Self-Constructing 100% Water-Resistant Metal Sulfides through In Situ Acid Etching for Effective Elemental Mercury (Hg0) Capture.

Metal sulfides (MSs) can efficiently entrap thiophilic components, such as elemental mercury (Hg0), and realize environmental remediation. However, there is still a critical problem challenging the extensive application of MSs in related areas, i.e., how to self-regulate their water (H2O) resistance without complexing the sorbent preparation procedure. This work for the first time developed an in situ acid-etching method that self-engineered the water affinity of MSs through changing the interfacial interaction between MSs and Hg0/H2O. The introduction of abundant, undercoordinated sulfur onto the sorbent surface was the primary reason accounting for the significantly improved H2O resistance. The high surface coverage of undercoordinated sulfur induced the formation of polysulfur chains (Sx2-) that stabilized Hg0 via a bridging bond and repelled H2O, attributed to the favorable electron configurations. These properties made the surface of MSs highly hydrophobic and increased the adsorption selectivity toward Hg0 over H2O. The MSs exhibited 100% H2O resistance even in the presence of 20% H2O, which is much higher than the H2O concentration under most practical scenarios. From these perspectives, this work for the first time overcame the detrimental effects of H2O on MSs through a self-regulating way that is scalable and negligibly complexes the sorbent preparation pathway. The highly water-resistant and cost-effective MSs as prepared can serve as efficient Hg0 removal from industrial flue gas in the future.

Prediction of arsenic adsorption onto metal organic frameworks and adsorption mechanisms interpretation by machine learning.

Metal-organic frameworks (MOFs) are promising adsorbents for the removal of arsenic (As) from wastewater. The As removal efficiency is influenced by several factors, such as the textural properties of MOFs, adsorption conditions, and As species. Examining all of the relevant factors through traditional experiments is challenging. To predict the As adsorption capacities of MOFs toward organic, inorganic, and total As and reveal the adsorption mechanisms, four machine learning-based models were developed, with the adsorption conditions, MOF properties, and characteristics of different As species as inputs. The results demonstrated that the extreme gradient boosting (XGBoost) model exhibited the best predictive performance (test R2 = 0.93-0.96). The validation experiments demonstrated the high accuracy of the inorganic As-based XGBoost model. The feature importance analysis showed that the concentration of As, the surface area of MOFs, and the pH of the solution were the three key factors governing inorganic-As adsorption, while those governing organic-As adsorption were the concentration of As, the pHpzc value of MOFs, and the oxidation state of the metal clusters. The formation of coordination complexes between As and MOFs is possibly the major adsorption mechanism for both inorganic and organic As. However, electrostatic interaction may have a greater effect on organic-As adsorption than on inorganic-As adsorption. Overall, this study provides a new strategy for evaluating As adsorption on MOFs and discovering the underlying decisive factors and adsorption mechanisms, thereby facilitating the investigation of As wastewater treatment.

Deep dewatering of refinery oily sludge by Fenton oxidation and its potential influence on the upgrading of oil phase.

Highly efficient dewatering is essential to the reduction and reclamation disposal of oily sludge, which is a waste from the extraction, transportation, and refining of crude oil. How to effectively break the water/oil emulsion is a paramount challenge for dewatering of oily sludge. In this work, a Fenton oxidation approach was adopted for the dewatering of oily sludge. The results show that the oxidizing free radicals originated from Fenton agent effectively tailored the native petroleum hydrocarbon compounds into smaller organic molecules, hence destructing the colloidal structure of oily sludge and decreasing the viscosity as well. Meanwhile, the zeta potential of oily sludge was increased, implying the decrease of repulsive electrostatic force to realize easy coalescence of water droplets. Thus, the steric and electrostatic barriers which restrained the coalescence of dispersed water droplets in water/oil emulsion were removed. With these advantages, the Fenton oxidation approach derived the significant decrease of water content, in which 0.294 kg water was removed from per kilogram oily sludge under the optimal operation condition (i.e., pH value of 3, solid-liquid ratio of 1:10, Fe2+ concentration of 0.4 g/L and H2O2/Fe2+ ratio of 10:1, and reaction temperature of 50 °C). In addition, the quality of oil phase was upgraded after Fenton oxidation treatment accompanying with the degradation of native organic substances in oily sludge, and the heating value of oily sludge was increased from 8680 to 9260 kJ·kg-1, which would facilitate to the subsequent thermal conversion like pyrolysis or incineration. Such results demonstrate that the Fenton oxidation approach is efficient for the dewatering as well as the upgrading of oily sludge.

In situ acid etching boosts mercury accommodation capacities of transition metal sulfides.

Transition Metal sulfides (TMSs) are effective sorbents for entrapment of highly polluting thiophiles such as elemental mercury (Hg0). However, the application of these sorbents for mercury removal is stymied by their low accommodation capacities. Among the transition metal sulfides, only CuS has demonstrated industrially relevant accommodation capacity. The rest of the transition metal sulfides have 100-fold lower capacities than CuS. In this work, we overcome these limitations and develop a simple and scalable process to enhance Hg0 accommodation capacities of TMSs. We achieve this by introducing structural motifs in TMSs by in situ etching. We demonstrate that in situ acid etching produces TMSs with defective surface and pore structure. These structural motifs promote Hg0 surface adsorption and diffusion across the entire TMSs architecture. The process is highly versatile and the in situ etched transition metal sulfides show over 100-fold enhancement in their Hg0 accommodation capacities. The generality and the scalability of the process provides a framework to develop TMSs for a broad range of applications.

Machine learning for hydrothermal treatment of biomass: A review.

Hydrothermal treatment (HTT) (i.e., hydrothermal carbonization, liquefaction, and gasification) is a promising technology for biomass valorization. However, diverse variables, including biomass compositions and hydrothermal processes parameters, have impeded in-depth mechanistic understanding on the reaction and engineering in HTT. Recently, machine learning (ML) has been widely employed to predict and optimize the production of biofuels, chemicals, and materials from HTT by feeding experimental data. This review comprehensively analyzed the application of ML for HTT of biomass and systematically illustrated basic ML procedure and descriptors for inputs and outputs of ML models (e.g., biomass compositions, operation conditions, yield and physicochemical properties of derived products) that could be applied in HTT. Moreover, this review summarized ML-aided HTT prediction of yield, compositions, and physicochemical properties of HTT hydrochar or biochar, bio-oil, syngas, and aqueous phase. Ultimately, future prospects were proposed to enhance predictive performance, mechanistic interpretation, process optimization, data sharing, and model application during ML-aided HTT.

Machine learning assisted predicting and engineering specific surface area and total pore volume of biochar.

Biochar produced from pyrolysis of biomass is a platform porous carbon material that have been widely used in many areas. Specific surface area (SSA) and total pore volume (TPV) are decisive to biochar application in hydrogen uptake, CO2 adsorption, and organic pollutant removal, etc. Engineering biochar by traditional experimental methods is time-consuming and laborious. Machine learning (ML) was used to effectively aid the prediction and engineering of biochar properties. The prediction of biochar yield, SSA, and TPV was achieved via random forest (RF) and gradient boosting regression (GBR) with test R2 of 0.89-0.94. ML model interpretation indicates pyrolysis temperature, biomass ash, and volatile matter were the most important features to the three targets. Pyrolysis parameters and biomass mixing ratios for biochar production were optimized via three-target GBR model, and the optimum schemes to obtain high SSA and TPV were experimentally verified, indicating the great potential of ML for biochar engineering.

Machine learning prediction of nitrogen heterocycles in bio-oil produced from hydrothermal liquefaction of biomass.

Hydrothermal liquefaction (HTL) of high-moisture biomass or biowaste to produce bio-oil is a promising technology. However, nitrogen-heterocycles (NH) presence in bio-oil is a bottleneck to the upgrading and utilization of bio-oil. The present study applied the machine learning (ML) method (random forest) to predict and help control the bio-oil NH, bio-oil yield, and N content of bio-oil (N_oil). The results indicated that the predictive performance of the yield and N_oil were better than previous studies, achieving test R2 of 0.92 and 0.95, respectively. Acceptable predictive performance (test R2 of 0.82 and RMSE of 7.60) for the prediction of NH was also achieved. The feature importance analysis, partial dependence, and Shapely value were used to interpret the prediction models and study the NH formation mechanisms and behavior. Then, forward optimization of NH was implemented based on optimal predictive models, indicating the high potential of ML-aided bio-oil production and engineering.

Machine learning predicting wastewater properties of the aqueous phase derived from hydrothermal treatment of biomass.

Hydrothermal treatment (HTT) is a potential technology for producing biofuel from wet biomass. However, the aqueous phase (AP) is generated inevitably in the process of HTT, and studies are lacking on the detailed exploration of AP properties. Therefore, machine learning (ML) models were built for predicting the pH, total nitrogen (TN), total organic carbon (TOC), and total phosphorus (TP) of the AP based on biomass feedstock and HTT parameters. Results showed that the gradient boosting decision tree (average testing R2 0.85-0.96) can accurately predict the above wastewater properties for both single- and multi-target models. ML-based feature importance indicated that nitrogen content of biomass, solid content, and temperature were the top three critical features for pH, TN, and TP, while those for TOC were reaction time, lipid, and temperature. This ML approach provides new insights to understand the formation and properties of the HTT AP by ML rather than time-consuming experiments.

Co-liquefaction of Chlorella and soybean straw for production of bio-crude: Effects of reusing aqueous phase as the reaction medium.

Co-liquefaction was combined with hydrothermal liquefaction (HTL) aqueous phase (AP) recirculation to improve the practicality of HTL process. The Chlorella powder (CL), soybean straw (SS), and their mixture (CS) with ratio 1:1 were processed at 300 °C for 20 min, and the AP was recirculated four times. The yield of CS bio-crude was increased (from 24.28% to 31.83%) by co-liquefaction, but remained stable during AP recirculation. By contrast, the yields were increased for CL bio-crude (from 32.40% to 41.19%), SS hydrochar (from 19.55% to 30.88%), and CS hydrochar (from 9.42% to 14.76%) by recirculation. The elemental analysis, chemical composition analysis, functionality analysis, thermogravimetric analysis, and verification experiments (HTL with model AP components) show the N-containing compounds (e.g., amines) in AP were converted into amides (acylation) for CL bio-crude, into N-heterocycles (Maillard reactions) for CS hydrochar, and into Mannich bases for SS hydrochar, which contributed to the increased yield and N content (from 7.27% to 8.82% for CL bio-crude). Furthermore, the O content of CS bio-crude was decreased (from 15.31% to 12.52%) by recirculation, resulted from the conversion of N-heterocyclic ketones into pyrazine derivates. The decreased O content and comprehensive combustibility index (from 0.306 to 0.177) of CS bio-crude indicate the great potential of this craft combination.

Machine learning prediction and optimization of bio-oil production from hydrothermal liquefaction of algae.

Hydrothermal liquefaction (HTL) of algae is a promising biofuel production technology. However, it is always difficult and time-consuming to identify the best optimal conditions of HTL for different algae by the conventional experimental study. Therefore, machine learning (ML) algorithms were applied to predict and optimize bio-oil production with algae compositions and HTL conditions as inputs, and bio-oil yield (Yield_oil), and the contents of oxygen (O_oil) and nitrogen (N_oil) in bio-oil as outputs. Results indicated that gradient boosting regression (GBR, average test R2 âˆ¼ 0.90) exhibited better performance than random forest (RF) for both single and multi-target tasks prediction. Furthermore, the model-based interpretation suggested that the relative importance of operating conditions (temperature and residence time) was higher than algae characteristics for the three targets. Moreover, ML-based reverse and forward optimizations were implemented with experimental verifications. The verifications were acceptable, showing great potential of ML-aided HTL for producing desirable bio-oil.

Co-culture of fungi-microalgae consortium for wastewater treatment: A review.

The treatment of wastewater by microalgae has been studied and proved to be effective through previous studies. Due to the small size of microalgae, how to efficiently harvest microalgae from wastewater is a crucial factor restricting the development of algal technologies. Fungi-assisted microalgae bio-flocculation for microalgae harvesting and wastewater treatment simultaneously, which was overlooked previously, has attracted increasing attention in the recent decade due to its low cost and high efficiency. This review found that fungal hyphae and microalgae can stick together due to electrostatic neutralization, surface protein interaction, and exopolysaccharide adhesion in the co-culture process, realizing co-pelletization of microalgae and fungi, which is conducive to microalgae harvesting. Besides, the combination of fungi and microalgae has a complementary effect on pollutant removal from wastewaters. The co-culture of fungi-microalgae has excellent development prospects with both environmental and economic benefits, and it is expected to be applied on an industrial scale.

An overview on engineering the surface area and porosity of biochar.

Surface area and porosity are important physical properties of biochar, playing a crucial role in many biochar applications, such as wastewater treatment and soil remediation. The production of engineered biochar with highly porous structure and large surface area has received extensive attention. This paper comprehensively reviewed the effects of biomass and pyrolysis parameters on the surface area and porosity of biochar. The composition of biomass feedstock and pyrolysis temperature are the major influencing factors. It is suggested that the lignocellulosic biomass is an outstanding candidate, wood and woody biomass in particular. Besides, moderate temperatures (400-700 °C) are suitable for the development of the pore structure. Further improvement can be implemented by additional treatments. Activation is the most widely used and effective way to promote biochar surface area and porosity, especially the chemical activation. Enhancement can also be achieved by using other treatment methods, such as carbonaceous materials coating, ball milling, and templating. Future research should focus on upgrading or developing treatment technology to achieve enhanced functionality and porous structure of biochar simultaneously.

A review on nitrogen transformation in hydrochar during hydrothermal carbonization of biomass containing nitrogen.

Biomass is a type of renewable and sustainable resource that can be used to produce various fuels, chemicals, and materials. Nitrogen (N) in biomass such as microalgae should be reduced if it is used to produce fuels, while the retention of N is favorable if the biomass is processed to yield chemicals or materials with N-containing functional groups. The engineering of the removal and retention of N in hydrochar during hydrothermal carbonization (HTC) of biomass rich in protein is a research hot spot in the past decade. However, the N transformation during HTC has not yet been fully understood. In order to mediate the migration and transformation of N in hydrochar, the present review overviewed i) the characteristics of hydrochar and the original feedstock, ii) the possible N transformation behavior and mechanisms, and iii) the effect of factors such as feedstock and pyrolysis parameters such as temperature on hydrochar N. The high temperature and high protein content promote the dehydration, decarboxylation, and deamination of biomass to produce hydrochar solid fuel with reduced N content, while the Millard and Mannich reactions for lignocellulosic biomass rich in carbohydrate (cellulose, hemicellulose, and lignin) at medium temperatures (e.g., 180-240 °C) significantly promote the enrichment of N in hydrochar. The prediction models can be built based on properties of biomass and the processing parameters for the estimation of the yield and the content of N in hydrochar.

Bioenergy recovery from wastewater produced by hydrothermal processing biomass: Progress, challenges, and opportunities.

Hydrothermal carbonization (HTC)/liquefaction (HTL)/gasification (HTG) are promising processes for biofuel production from biomass containing high moisture. However, wastewater, the aqueous phase (AP) byproduct from these hydrothermal processes, is inevitably produced in large amounts. The AP contains >20% of the biomass carbon, and the total organic carbon in AP is as high as 10-20 g/L. The treatment and utilization of AP are becoming a bottleneck for the industrialization of hydrothermal technologies. The major challenges are the presence of various inhibitory substances and the high complexity of AP. Bioenergy recovery from AP has attracted increasing interest. In the present review, the compositions and characteristics of AP are first presented. Then, the progress in recovering bioenergy from AP by recirculation as the reaction solvent, anaerobic digestion (AD), supercritical water gasification (SCWG), microbial fuel cell (MFC), microbial electrolysis cell (MEC), and microalgae cultivation is discussed. Recirculation of AP as reaction solvent is preferable for AP from biomass with relatively low moisture; AD, MFC/MEC, and microalgae cultivation are desirable for the treatment of AP produced from processing biomass with low lignin content at relatively low temperatures; SCWG is widely applicable but is energy-intensive. Finally, challenges and corresponding strategies are proposed to promote the development of AP valorization technologies. Comprehensive analysis of AP compositions, clarification of the mechanisms of valorization processes, valorization process integration detoxification of AP, polycultures and co-processing of AP with other waste, enhancement in pollutant removal, scaling-up performance, and the techno-economic analysis and life-cycle assessment of valorization systems are promising directions in future investigations.

The effect of aqueous phase recirculation on hydrothermal liquefaction/carbonization of biomass: A review.

Aqueous phase (AP) recirculation is attracting increasing interest in hydrothermal process field as it has the potential to increase the yield of bio-crude and/or hydrochar and decrease the cost of hydrothermal wastewater disposal. This work summarizes the effect of AP recirculation on hydrothermal processing biomass, including the discussions on the mechanisms account for the increased yield and the changing properties of the hydrochar and bio-crude. However, the application of AP recirculation in hydrothermal process is limited by the enrichment of nitrogen in bio-crude and the applicability of only specific biomass type. To alleviate these limitations, the feasibility of combining AP recirculation with other strategies (e.g., co-solvent and co-feed) has been discussed. The possibility of using AP as a resource (e.g., nutrient source, and material mediator) can be increased by AP recirculation due to the accumulation of substances.

A review on pyrolysis of protein-rich biomass: Nitrogen transformation.

Pyrolysis of protein-rich biomass, such as microalgae, macroalgae, sewage sludge, energy crops, and some lignocellulosic biomass, produces bio-oil with high nitrogen (N) content, sometimes as high as 10 wt% or even higher. Major nitrogenous compounds in bio-oil include amines/amides, N-containing heterocycles, and nitriles. Such bio-oil cannot be used as fuel directly since the high N content will induce massive emission of nitrogen oxides during combustion. The present review comprehensively summarized the effects of biomass compositions (i.e., elemental, biochemical, and mineral compositions) and pyrolysis parameters (i.e., temperature, heating rate, atmosphere, bio-oil collection/fractionation methods, and catalysts) on the contents of N and the N-containing chemical components in bio-oil. The migration and transformation mechanisms of N during the pyrolysis of biomass were then discussed in detail. Finally, the research gaps were identified, followed by the proposals for future investigations to achieve the denitrogenation of bio-oil.