The John Charnley Award: an accurate and extremely sensitive method to separate, display, and characterize wear debris: part 2: metal and ceramic particles

Abstract

Background: Metal-on-metal and ceramic-on-ceramic bearings were introduced as alternatives to conventional polyethylene in hip arthroplasties to reduce wear. Characterization of wear particles has been particularly challenging due to the low amount and small size of wear particles. Current methods of analysis of such particles have shortcomings, including particle loss, clumping, and inaccurate morphologic and chemical characterization.

Questions/purposes: We describe a method to recover and characterize metal and ceramic particles that (1) improves particle purification, separation, and display; (2) allows for precise particle shape characterization; (3) allows accurate chemical identification; and (4) minimizes particle loss.

Methods: After enzymatic digestion, a single pass of ultracentrifugation cleaned and deposited particles onto silicon wafers or grids for imaging analysis. During centrifugation, particles were passed through multiple layers of denaturants and a metal-selective high-density layer that minimized protein and nucleic acid contamination. The protocol prevented aggregation, providing well-dispersed particles for chemical and morphologic analysis. We evaluated the efficacy and accuracy of this protocol by recovering gold nanobeads and metal and ceramic particles from joint simulator wear tests.

Results: The new protocol recovered particles ranging in size from nanometers to micrometers and enabled accurate morphologic and chemical characterization of individual particles.

Conclusion: Both polyethylene and metal wear debris can be simultaneously analyzed from the same sample by combining a silicon wafer display protocol for polyethylene and the metal and ceramics silicon wafer display protocol.

Clinical relevance: Accurate analysis of wear debris is essential in understanding the processes that produce debris and a key step in development of more durable and biocompatible implants.

Fig. 1

A flowchart illustrates an outline of the experiment.

Fig. 2

A schematic diagram outlines the MC-SWD protocol.

Fig. 3

FE-SEM images show MOM hip particles collected on a silicon wafer, demonstrating the even distribution of particles on the wafer. As a general rule, the biggest and smoother particles contain Co (A) whereas the large particles (B) and the majority of the smaller particles (C) with a rougher surface contain CrOx. The smallest particles (white arrows) were less than 5 nm in size. The inset shows an example of rod-shaped particles (white arrows); note difference in magnification.

Fig. 4A–B

(A) A 12-nm CrOx particle from a MOM hip sample is shown. (B) The EDS spectrum of this particle is shown.

Fig. 5A–B

Graphs show a comparison of P-P plots for d_max from two different areas on a wafer (A and B), confirming the uniform distribution of particles. Cum Prob = cumulative probability.

Fig. 6

An image shows metal particles on a TEM grid (STEM detector). Amorphous CrOx particles are light gray and crystalline Co-rich particles are dark gray. Predominantly amorphous or crystalline particles are easily recognized, as well as subregions that are more or less crystalline inside a single particle. Furthermore, access to a monolayer of particles assures the image is not the result of superimposed particles.

Fig. 7A–B

Graphs show the chemical distribution of the particles for (A) MOM and (B) MOC samples.

Fig. 8A–B

Graphs show, for MOC low-wear samples, precise chemical characterization and discrimination are critical to avoid skewed distributions as shown in MOC samples (A) with and (B) without contaminants.

Fig. 9

An image shows chemical characterization of particles on a wafer for a MOC hip sample. Due to the extremely low wear of the ceramic counterface, no particles containing Al were detected in this field.

Fig. 10A–B

Graphs show shape distribution as a function of maximum Feret’s diameter (d_max) for a typical MOM sample. Particle shapes were automatically extracted via a dedicated algorithm and could be associated to any other parameter. (A) A distribution of d_max for various shapes is shown. (B) Peaks in the distribution of particles with various shapes are more readily identified in this line graph; round particles are usually the smallest particles whereas irregular particles are larger; oval particles and rods are more dispersed across the entire spectrum.

Fig. 11A–C

Images show the chemical characterization of particles on a wafer. (A) On a low-resolution image of the analyzed wafer area acquired via EDS software, particles were automatically identified (marked on the image) and numbered; some particles cannot be distinguished from the marks because of the low resolution and small particle dimension. Spectra are shown from the particles in the circle in (A), specifically from (B) Point 17, which is a Co-rich particle and dark, and (C) Point 19, which is from a particle mainly composed of CrOx and bright.