Figures (7)  Tables (6)

    • Figure 1. 

      Comparison of two preparation processes of biochar.

    • Figure 2. 

      Microstructure and function of biochar.

    • Figure 3. 

      Keyword co-occurrence network (2020–2025) visualized using VOSviewer. Node size represents keyword frequency; line thickness indicates co-occurrence strength. Colors denote thematic clusters: red (remediation & engineered biochar), green (adsorption & modeling), blue (material synthesis), yellow (sustainability & policy).

    • Figure 4. 

      Application of biochar in water pollution control.

    • Figure 5. 

      Machine learning classification and its application to biochar.

    • Figure 6. 

      Applications of machine learning algorithms in biochar research.

    • Figure 7. 

      Machine learning-based biochar life cycle analysis (LCA) phased flow diagram.

    • Feedstock type Carbon content Ash content Surface area
      (m2/g)      		 Pore size
      (nm)      		 pH Porosity CEC
      (cmol/kg)      		 Bulk density
      (g/cm3)      		 Ref.
      Wood 75%−85% 2%−5% 300−600 2−10 7−9 65%−75% 15−40 0.25−0.40 [37−41]
      Crop residues 60%−75% 5%−15% 100−400 5−20 6−8 55%−65% 20−30 0.30−0.50 [38−44]
      Sludge 30%−50% 20%−40% 50−200 10−50 5−7 40%−55% 4−35 0.45−1.50 [38,45−48]
      Food waste 40%−60% 10%−30% 50−150 15−40 4−6 50%−65% 15−25 0.35−0.60 [49−55]
      Animal manure 35%−55% 25%−45% 80−250 5−30 7−10 45%−60% 15−140 0.30−0.50 [48,56−59]

      Table 1. 

      Effects of different raw materials on biochar properties

    • Parameter Pyrolysis Hydrothermal carbonization Ref.
      Temperature range 300–800 °C 180–250 °C [74−76]
      Reaction environment Oxygen-limited or anaerobic High-temperature, high-pressure water [60,76]
      Main products Biochar, syngas, bio-oil Hydrochar, liquid by-products [60,76]
      By-products Gases (CO2, H2, CH4), tar Soluble organic compounds, acidic substances [34,77]
      Biochar properties High carbon content, stable structure, highly porous High carbon content, stable structure, highly porous [78]
      Suitable feedstock Woody biomass, agricultural waste, sludge High-moisture biomass (food waste, sewage sludge, animal wastes) [66,79]
      Advantages High carbon sequestration efficiency, stable biochar Suitable for wet biomass, no drying needed, rich in functional groups [63,66,67,80]
      Disadvantages Requires high temperatures, energy-intensive Lower carbon sequestration, less stable biochar [63,80]

      Table 2. 

      Two methods of biochar preparation

    • Modification method Change in surface area Functional group changes Application field Ref.
      CO2 activation 50%–100% Increased carboxyl, phenol groups Water pollution treatment [99,100]
      Fe3+ doping 20%–60% Increased catalytic active sites Catalytic degradation of pollutants [92]
      Sulfonation 10%–40% Increased SO3H groups Acidic catalysis [101−103]
      KOH activation 80%–150% Enhanced hydroxyl, carbonyl groups CO2 capture, energy storage [104,105]
      N-doping 20%–80% Introduced amine, pyridinic-N Electrochemical catalysis [106−110]

      Table 3. 

      Effects of different biochar modification methods on its properties

    • Composite type Specific surface area (m2/g) Functional properties Application scenarios Performance improvement Ref.
      Graphene-biochar 800–1,200 High conductivity, catalytic activity Supercapacitors, electrocatalysis 200%–300% [120]
      Fe3O4-loaded biochar 300–500 Magnetic recovery, redox capacity Heavy metal adsorption, Fenton reaction 150%–200% [121,122]
      N-doped porous biochar 600–900 High nitrogen content, alkaline sites CO2 capture, Soil pH regulation 80%–120% [107,123]
      Chitosan-biochar membrane 50–150 Antibacterial property, biodegradability Water treatment 90%–130% [124]
      Fe/Cu bimetallic-loaded biochar 200–400 Bimetallic synergistic adsorption,
      magnetic recovery      		 High-efficiency Pb2+/Cd2+ adsorption 400%–500% [125]

      Table 4. 

      Performance and applications of novel biochar composite materials

    • Feedstock type Pyrolysis
      temperature (°C)      		 Carbon
      sequestration rate      		 Total GHG emission reduction rate Key mechanisms Ref.
      Crop residues 400–600 60%–75% 40%–60% Inhibition of denitrification enzyme activity; NH4+ adsorption [159,160]
      Municipal sludge 300–500 40%–50% 30%–50% Heavy metal immobilization; NO3− adsorption; pH regulation [161,162]
      Wood waste 500–700 85%–90% 20%–40% Landfill diversion; Physical barrier formation to delay decomposition [163]
      Food waste 250–400 30%–45% 25%–45% Promotion of methanotroph proliferation; C/N ratio adjustment [164,165]
      Algal biomass 500–700 75%–85% 40%–60% High-temperature stabilized carbon structure, promotes soil carbon-fixing microbial communities [166,167]
      Poultry manure 300–500 50%–60% 35%–50% Reduces N2O emissions, adsorbs NH3 [168,169]

      Table 5. 

      Correlation between biochar feedstock types and carbon sequestration-emission reduction efficiency

    • Method type Application direction Representative applications Technical advantages Ref.
      CNN Image structure recognition Predicting specific surface area and pore size Extracts microstructural image features [212]
      RNN Dynamic data modeling Analyzing performance variations during pyrolysis Adapts to time-series data [206]
      Multi-objective optimization Comprehensive performance optimization Simultaneously optimizing yield, adsorption rate,
      and energy consumption      		 Collaborative optimization with high efficiency [213]
      GA/PSO Rapid process optimization Identifying optimal pyrolysis temperature and modifier dosage Broad search range & rapid convergence [214]
      MTDL (Multi-task
      deep learning)      		 Multi-objective collaborative optimization Simultaneously optimizing biochar's adsorption
      of cadmium (Cd) and methane (CH4) emission reduction efficiency      		 Cross-task parameter sharing, maximization of synergistic effects [215]

      Table 6. 

      Typical applications of deep learning and multi-objective optimization in biochar research