MRS Bulletin

Papers
(The H4-Index of MRS Bulletin is 29. The table below lists those papers that are above that threshold based on CrossRef citation counts [max. 250 papers]. The publications cover those that have been published in the past four years, i.e., from 2022-08-01 to 2026-08-01.)
ArticleCitations
Amphiphilic assembly enhances performance of biofuel cells189
Two-and-one-half cheers for the mediocracy*79
Things I didn’t and still don’t fully understand77
EU publishes guidance for safe and sustainable materials and chemicals64
Journal Highlights57
MRS Bulletin turns 50!54
Failure-resistant mechanisms in nature inform metamaterials design53
Metallic Zn crystals grown in liquid Ga imitate snowflake patterns49
Forming and erasing memories in disordered solids43
Gas-phase materials synthesis in environmental transmission electron microscopy42
Closing a chapter with MRS Bulletin and MRS39
ML drives design of materials with ultrahigh strength-to-weight ratios37
Fuel cell-transistor combination amplifies signals, expands biosensing applications36
Engineering of a novel SilkMA-bacterial cellulose hydrogel bioink for digital light processing three-dimensional bioprinting36
Beyond potentials: Integrated machine learning models for materials35
Journal Highlights34
Solution-driven bioinspired design: Themes of latch-mediated spring-actuated systems34
How to build an effective self-driving laboratory33
Micro-architected material design for mechanical response33
Closing the sustainability gap in materials education33
Advancing materials science through data integration and sustainable innovation33
Journal Highlights32
Microstructure development and design in deformation-based metal additive manufacturing32
Charged-sorbents overcome current limitations of CO2 capture30
Going cubic halves the efficiency droop in InGaAlN light-emitting diodes30
Inorganic nanotubes: From WS2 to “misfit” layered compounds29
Journal Highlights29
Journal Highlights29
NIST issues broad agency announcement for proposals to advance microelectronics technologies29
Kinetic network models to study molecular self-assembly in the wake of machine learning29
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