Table 1 Comparison.

From: Efficient and optimized blood cancer detection using engineered graphene-based silicon–TiN–silicon multilayered plasmonic sensor design with behaviour prediction using machine learning

Design

Sensitivity (nm/RIU)

Quality factor (Q)

Figure of merit (FOM)

Detection limit (DL)

38

253

–

–

–

39

349

–

–

–

40

1000

5166

2.94

0.04

41

74.5

19.82

–

–

42

153

–

–

–

43

161

–

–

–

44

690

2500

1400

–

45

200

3493

222

–

46

1000

–

–

–

47

500

–

–

–

48

1100

–

3.832

0.391

49

387

–

–

–

50

596

–

–

–

51

900

190

105

0.00001

52

400

–

–

–

53

546.72

2066.44

–

1.44

54

1000

–

–

–

55

1400

–

–

–

56

306.25

5000

103

–

57

–

262

–

0.002

58

359

–

–

–

Proposed blood cancer design

1430

125

121

0.044

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