OOIR: Observatory of International Research

Papers

(The H4-Index of MRS Bulletin is 27. 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 2022-06-01 to 2026-06-01.)

Article	Citations
Amphiphilic assembly enhances performance of biofuel cells	177
Two-and-one-half cheers for the mediocracy*	76
Things I didn’t and still don’t fully understand	70
Dislocation does the twist with a double helix	65
EU publishes guidance for safe and sustainable materials and chemicals	57
Journal Highlights	54
MRS Bulletin turns 50!	52
Failure-resistant mechanisms in nature inform metamaterials design	50
Advancing materials science through data integration and sustainable innovation	47
Closing the sustainability gap in materials education	46
Metallic Zn crystals grown in liquid Ga imitate snowflake patterns	45
Forming and erasing memories in disordered solids	42
Gas-phase materials synthesis in environmental transmission electron microscopy	41
Closing a chapter with MRS Bulletin and MRS	39
ML drives design of materials with ultrahigh strength-to-weight ratios	38
Engineering of a novel SilkMA-bacterial cellulose hydrogel bioink for digital light processing three-dimensional bioprinting	33
Fuel cell-transistor combination amplifies signals, expands biosensing applications	33
Solution-driven bioinspired design: Themes of latch-mediated spring-actuated systems	32
How to build an effective self-driving laboratory	32
Journal Highlights	32
Beyond potentials: Integrated machine learning models for materials	32
Functional imaging of brain organoids using high-density microelectrode arrays	31
Microstructure development and design in deformation-based metal additive manufacturing	31
Journal Highlights	30
Going cubic halves the efficiency droop in InGaAlN light-emitting diodes	29

NIST issues broad agency announcement for proposals to advance microelectronics technologies	28
Charged-sorbents overcome current limitations of CO2 capture	28
Kinetic network models to study molecular self-assembly in the wake of machine learning	27
Journal Highlights	27