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Rehman et al. Energy Mater 2024;4:400068  https://dx.doi.org/10.20517/energymater.2024.06                                  Page 51 of 64

                        Composite
                1D      Bi nanowires@graphene   Vacuum filtration assembly  1M NaPF  in   -   82.7 276  1     1,000           295        5       [316]
                                                                    6
                        film                                  DME
                        Bi nanotubes     Iodine-ion-assisted galvanic   1M NaPF  in   AM:carbon black:CMC   69.2 355  20  15,000  319    150     [204 ]
                                                                    6
                                         replacement          DME         (90:5:5)
                2D      Bi@N-doped carbon  Solvothermal and carbonization  1M NaPF  in   AM:carbon black:PVDF   85.1  ~325  10  5,000  341.5  10  [223]
                                                                    6
                                                              DME         (80:10:10)
                3D      Porous Bi/C      Annealing            1M NaPF  in   AM:carbon black: sodium   95.2 178  50  20,000    101        72      [317]
                                                                    6
                                                              DME         alginate (70:15:15)
                        LC-bi composite  Calcination          1M NaPF  in   AM:super P:PVDF (80:10:10)  53.4 225.6  1  2,600  236.1      100     [201]
                                                                    6
                                                              DME
                        3D porous Bi     Solution reduction   1M NaPF  in   AM:acetylene black:PVDF   65.9 374  10  3,000     354        60      [318]
                                                                    6
                                                              DME         (70:20:10)
                        Bulk Bi          Commercial           1M NaPF  in   AM:super P:PVDF (80:10:10)  94.8 389  0.4  2,000  356        2       [319]
                                                                    6
                                                              diglyme
               To ascertain capabilities of alloying anodes for next-generation SIBs, many modifications are necessary. Optimized alloying anode’s structure, composition,
               matching cathodes, binders, and electrolyte systems need extensive sorting. Structural modification is a prerequisite to accommodate some alloying anodes'
               volume changes, voltage hysteresis, and low electronic conductivity. However, effective modulation from the commercial perspective may take years to see
               SIBs on the energy horizon. Without deep tunneling into mechanistic paths and limitations, it seems unlikely.

               Nanosized composite/alloy hybrid design with porous 3D electronically conductive materials must focus on alloy compositions for highly stable electrode
               configurations. Similarly, many recent studies have extensively validated the superiority of ether-based solvent systems as they offer stable and low energy
                                        +
               solvation structuring with Na , superior wettability, electrolyte penetration, and fast and reversible (de)sedation kinetics, ensuring thin and stable SEI. These
               ether-based electrolyte systems should be widely explored for various SIB alloying anodes along with recent approaches using binder-free and anodeless
               configurations and shifting towards solid electrolytes with potential to direct the future of alloying anodes.


               Diverse formulations and materials choices in alloying SIB anodes have further been diversified with nanostructuring, doping, vacancy creation, alloying, and
               compositing with multicomponents, enhancing possibilities and prospects for high-stability electrode materials. In this regard, hard carbon materials with
               already proven promise as SIB anodes need more attention when compositing with alloying materials. A straightforward approach to optimize and validate an
               alloying-based electrode's performance from the bulk of theoretical and experimental literature reports is to use machine learning tools and theoretical models
               to obtain more efficient methodologies and material combinations, thereby reducing weary efforts in random material/methodology selection.
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