lambda(nm) n_ito k_ito (dispersion formula fit to Woollam data) 200.0 3.8357 0.1620 205.0 3.5822 0.1292 210.0 3.3857 0.1069 215.0 3.2282 0.0909 220.0 3.0989 0.0790 225.0 2.9906 0.0697 230.0 2.8985 0.0624 235.0 2.8191 0.0565 240.0 2.7499 0.0516 245.0 2.6889 0.0475 250.0 2.6348 0.0441 255.0 2.5865 0.0411 260.0 2.5430 0.0386 265.0 2.5037 0.0364 270.0 2.4679 0.0344 275.0 2.4352 0.0327 280.0 2.4052 0.0312 285.0 2.3776 0.0298 290.0 2.3520 0.0286 295.0 2.3283 0.0275 300.0 2.3062 0.0265 305.0 2.2856 0.0256 310.0 2.2663 0.0248 315.0 2.2483 0.0240 320.0 2.2312 0.0233 325.0 2.2152 0.0227 330.0 2.2001 0.0222 335.0 2.1857 0.0216 340.0 2.1721 0.0212 345.0 2.1591 0.0207 350.0 2.1468 0.0204 355.0 2.1351 0.0200 360.0 2.1238 0.0197 365.0 2.1131 0.0194 370.0 2.1028 0.0191 375.0 2.0929 0.0188 380.0 2.0834 0.0186 385.0 2.0742 0.0184 390.0 2.0654 0.0182 395.0 2.0568 0.0181 400.0 2.0486 0.0179 405.0 2.0406 0.0178 410.0 2.0329 0.0177 415.0 2.0253 0.0176 420.0 2.0181 0.0175 425.0 2.0110 0.0175 430.0 2.0041 0.0174 435.0 1.9973 0.0174 440.0 1.9908 0.0173 445.0 1.9843 0.0173 450.0 1.9781 0.0173 455.0 1.9719 0.0174 460.0 1.9659 0.0174 465.0 1.9601 0.0174 470.0 1.9543 0.0175 475.0 1.9486 0.0175 480.0 1.9430 0.0176 485.0 1.9376 0.0177 490.0 1.9322 0.0178 495.0 1.9268 0.0179 500.0 1.9216 0.0180 505.0 1.9164 0.0181 510.0 1.9113 0.0182 515.0 1.9063 0.0184 520.0 1.9013 0.0185 525.0 1.8964 0.0187 530.0 1.8915 0.0189 535.0 1.8867 0.0190 540.0 1.8819 0.0192 545.0 1.8771 0.0194 550.0 1.8724 0.0196 555.0 1.8678 0.0199 560.0 1.8631 0.0201 565.0 1.8585 0.0203 570.0 1.8539 0.0206 575.0 1.8494 0.0208 580.0 1.8449 0.0211 585.0 1.8403 0.0213 590.0 1.8359 0.0216 595.0 1.8314 0.0219 600.0 1.8269 0.0222 605.0 1.8225 0.0225 610.0 1.8181 0.0228 615.0 1.8136 0.0232 620.0 1.8092 0.0235 625.0 1.8048 0.0238 630.0 1.8004 0.0242 635.0 1.7960 0.0246 640.0 1.7917 0.0249 645.0 1.7873 0.0253 650.0 1.7829 0.0257 655.0 1.7785 0.0261 660.0 1.7741 0.0265 665.0 1.7697 0.0270 670.0 1.7653 0.0274 675.0 1.7610 0.0278 680.0 1.7566 0.0283 685.0 1.7522 0.0288 690.0 1.7477 0.0292 695.0 1.7433 0.0297 700.0 1.7389 0.0302 705.0 1.7345 0.0307 710.0 1.7300 0.0312 715.0 1.7256 0.0318 720.0 1.7211 0.0323 725.0 1.7166 0.0329 730.0 1.7121 0.0334 735.0 1.7076 0.0340 740.0 1.7031 0.0346 745.0 1.6985 0.0352 750.0 1.6940 0.0358 755.0 1.6894 0.0364 760.0 1.6848 0.0371 765.0 1.6802 0.0377 770.0 1.6756 0.0384 775.0 1.6710 0.0391 780.0 1.6663 0.0397 785.0 1.6616 0.0404 790.0 1.6569 0.0412 795.0 1.6522 0.0419 800.0 1.6474 0.0426 805.0 1.6427 0.0434 810.0 1.6379 0.0442 815.0 1.6330 0.0449 820.0 1.6282 0.0457 825.0 1.6233 0.0465 830.0 1.6184 0.0474 835.0 1.6135 0.0482 840.0 1.6086 0.0491 845.0 1.6036 0.0500 850.0 1.5986 0.0508 855.0 1.5936 0.0517 860.0 1.5885 0.0527 865.0 1.5834 0.0536 870.0 1.5783 0.0546 875.0 1.5732 0.0555 880.0 1.5680 0.0565 885.0 1.5628 0.0575 890.0 1.5576 0.0586 895.0 1.5523 0.0596 900.0 1.5470 0.0607 905.0 1.5417 0.0618 910.0 1.5363 0.0629 915.0 1.5309 0.0640 920.0 1.5255 0.0651 925.0 1.5200 0.0663 930.0 1.5145 0.0675 935.0 1.5090 0.0687 940.0 1.5034 0.0699 945.0 1.4978 0.0711 950.0 1.4922 0.0724 955.0 1.4865 0.0737 960.0 1.4808 0.0750 965.0 1.4750 0.0763 970.0 1.4692 0.0777 975.0 1.4634 0.0791 980.0 1.4575 0.0805 985.0 1.4516 0.0819 990.0 1.4456 0.0834 995.0 1.4396 0.0849 1000.0 1.4336 0.0864 1005.0 1.4275 0.0879 1010.0 1.4214 0.0895 1015.0 1.4152 0.0911 1020.0 1.4090 0.0927 1025.0 1.4027 0.0944 1030.0 1.3964 0.0961 1035.0 1.3901 0.0978 1040.0 1.3837 0.0996 1045.0 1.3772 0.1013 1050.0 1.3707 0.1032 1055.0 1.3642 0.1050 1060.0 1.3576 0.1069 1065.0 1.3510 0.1088 1070.0 1.3443 0.1108 1075.0 1.3375 0.1128 1080.0 1.3308 0.1148 1085.0 1.3239 0.1169 1090.0 1.3170 0.1190 1095.0 1.3101 0.1211 1100.0 1.3031 0.1233